{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KwkIfUglkDK5"
      },
      "source": [
        "# Install & Import Libraries"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kTRYGyaTj75D",
        "outputId": "779232a6-a5be-4013-8d1e-2cc7162ea3bb",
        "collapsed": true
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: nltk in /usr/local/lib/python3.12/dist-packages (3.9.1)\n",
            "Requirement already satisfied: wordcloud in /usr/local/lib/python3.12/dist-packages (1.9.4)\n",
            "Requirement already satisfied: seaborn in /usr/local/lib/python3.12/dist-packages (0.13.2)\n",
            "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.12/dist-packages (1.6.1)\n",
            "Requirement already satisfied: click in /usr/local/lib/python3.12/dist-packages (from nltk) (8.3.1)\n",
            "Requirement already satisfied: joblib in /usr/local/lib/python3.12/dist-packages (from nltk) (1.5.2)\n",
            "Requirement already satisfied: regex>=2021.8.3 in /usr/local/lib/python3.12/dist-packages (from nltk) (2025.11.3)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.12/dist-packages (from nltk) (4.67.1)\n",
            "Requirement already satisfied: numpy>=1.6.1 in /usr/local/lib/python3.12/dist-packages (from wordcloud) (2.0.2)\n",
            "Requirement already satisfied: pillow in /usr/local/lib/python3.12/dist-packages (from wordcloud) (11.3.0)\n",
            "Requirement already satisfied: matplotlib in /usr/local/lib/python3.12/dist-packages (from wordcloud) (3.10.0)\n",
            "Requirement already satisfied: pandas>=1.2 in /usr/local/lib/python3.12/dist-packages (from seaborn) (2.2.2)\n",
            "Requirement already satisfied: scipy>=1.6.0 in /usr/local/lib/python3.12/dist-packages (from scikit-learn) (1.16.3)\n",
            "Requirement already satisfied: threadpoolctl>=3.1.0 in /usr/local/lib/python3.12/dist-packages (from scikit-learn) (3.6.0)\n",
            "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.12/dist-packages (from matplotlib->wordcloud) (1.3.3)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.12/dist-packages (from matplotlib->wordcloud) (0.12.1)\n",
            "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.12/dist-packages (from matplotlib->wordcloud) (4.61.0)\n",
            "Requirement already satisfied: kiwisolver>=1.3.1 in /usr/local/lib/python3.12/dist-packages (from matplotlib->wordcloud) (1.4.9)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.12/dist-packages (from matplotlib->wordcloud) (25.0)\n",
            "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.12/dist-packages (from matplotlib->wordcloud) (3.2.5)\n",
            "Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.12/dist-packages (from matplotlib->wordcloud) (2.9.0.post0)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.12/dist-packages (from pandas>=1.2->seaborn) (2025.2)\n",
            "Requirement already satisfied: tzdata>=2022.7 in /usr/local/lib/python3.12/dist-packages (from pandas>=1.2->seaborn) (2025.2)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.12/dist-packages (from python-dateutil>=2.7->matplotlib->wordcloud) (1.17.0)\n",
            "✅ All libraries imported successfully.\n"
          ]
        }
      ],
      "source": [
        "!pip install nltk wordcloud seaborn scikit-learn\n",
        "# --- Imports ---\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import re\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from wordcloud import WordCloud\n",
        "import os\n",
        "\n",
        "# NLP\n",
        "import nltk\n",
        "from nltk.corpus import stopwords\n",
        "from nltk.tokenize import word_tokenize\n",
        "from nltk.stem import WordNetLemmatizer\n",
        "import re\n",
        "\n",
        "# ML\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "from sklearn.feature_extraction.text import TfidfVectorizer\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.pipeline import Pipeline\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.svm import LinearSVC\n",
        "from sklearn.metrics import accuracy_score, classification_report\n",
        "import lightgbm as lgb\n",
        "\n",
        "# Download required NLTK datasets (if not already downloaded)\n",
        "nltk.download('stopwords', quiet=True)\n",
        "nltk.download('punkt', quiet=True)\n",
        "nltk.download('wordnet', quiet=True)\n",
        "nltk.download('punkt_tab', quiet=True)\n",
        "\n",
        "\n",
        "print(\"✅ All libraries imported successfully.\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SH93EPbD2YKO",
        "outputId": "080bdf5c-a43f-490b-f103-32ad793ddf80"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading from https://www.kaggle.com/api/v1/datasets/download/abiyyurasyiq/mobile-legends-google-play-reviews?dataset_version_number=2...\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 3.54M/3.54M [00:01<00:00, 2.92MB/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting files...\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Path to dataset files: /root/.cache/kagglehub/datasets/abiyyurasyiq/mobile-legends-google-play-reviews/versions/2\n"
          ]
        }
      ],
      "source": [
        "import kagglehub\n",
        "\n",
        "# Download latest version\n",
        "path = kagglehub.dataset_download(\"abiyyurasyiq/mobile-legends-google-play-reviews\")\n",
        "\n",
        "print(\"Path to dataset files:\", path)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "vhTzsk0IGi1X",
        "outputId": "cbf49b25-8cbb-41d3-83aa-1cdd3c1a3964"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                               reviewId       userName  \\\n",
              "0  d9b3706c-a29d-4661-b757-812be9802f33  A Google user   \n",
              "1  cc717825-ea2b-444c-8e80-e4f517314880  A Google user   \n",
              "2  f3c5353d-65f8-486f-81b0-5ac733ed88a0  A Google user   \n",
              "3  5d2611d3-dfda-41b5-87e8-d20498b0f4a4  A Google user   \n",
              "4  38f96ac5-c264-4045-babf-543f576d70b2  A Google user   \n",
              "\n",
              "                                             content  score  thumbsUpCount  \\\n",
              "0  Love this game. Honestly didn't have any expec...      5              0   \n",
              "1    the graphics? 10/10!!! this game is so funn!! 😄      5              0   \n",
              "2  Horrible. your matchmaking sucks. The players ...      1              0   \n",
              "3  dark system. fix your match making, it makes t...      1              0   \n",
              "4  This game has a so much dark sistem and the li...      1              0   \n",
              "\n",
              "                    at  \n",
              "0  2025-06-10 13:20:23  \n",
              "1  2025-06-10 13:19:39  \n",
              "2  2025-06-10 13:18:52  \n",
              "3  2025-06-10 13:15:51  \n",
              "4  2025-06-10 13:11:34  "
            ],
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              "      <th>reviewId</th>\n",
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              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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              "        element.innerHTML = '';\n",
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              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "\n",
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            ],
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              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 52651,\n  \"fields\": [\n    {\n      \"column\": \"reviewId\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 52651,\n        \"samples\": [\n          \"a40aa31e-234a-4892-98e9-c5d5e747e441\",\n          \"04d58765-815c-4d42-971c-0f7e03b433ca\",\n          \"8556ebc5-eea9-45ed-b8f0-8d0474b28522\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"userName\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 557,\n        \"samples\": [\n          \"Mumu Mumu\",\n          \"baby kyungsoo\",\n          \"vianne venice\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"content\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 46910,\n        \"samples\": [\n          \"The Matchmaking is still worst, kept on getting teams that feed and all. I have to carry the team as a TANK/SUPPORT WITH NO DAMAGE.. And yes They will ALWAYS BLAME THE TANK AND SUPPORT.. BETTER FIX THIS ISSUE IF YOU WANT A HIGHER RATING. Moonton are just out to suck all our money but never hear to the genuine players\",\n          \"Bring back the old skill of Minotaur!\",\n          \"i love this game!!!\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 1,\n        \"max\": 5,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          1,\n          2,\n          4\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"thumbsUpCount\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 199,\n        \"min\": 0,\n        \"max\": 31339,\n        \"num_unique_values\": 292,\n        \"samples\": [\n          1392,\n          691,\n          49\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"at\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"num_unique_values\": 52117,\n        \"samples\": [\n          \"2025-05-19 00:13:46\",\n          \"2025-02-21 09:02:41\",\n          \"2025-05-11 18:59:32\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 3
        }
      ],
      "source": [
        "import pandas as pd\n",
        "\n",
        "df = pd.read_csv(path + '/mobile_legends_reviews.csv')\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eMYOscv-kaNb"
      },
      "source": [
        "# Dataset Information"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DoDY9IUnkakg",
        "outputId": "93da0a8b-a18e-4215-9b3c-4a02f7f9f8df"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "📋 Dataset Info:\n",
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 52651 entries, 0 to 52650\n",
            "Data columns (total 6 columns):\n",
            " #   Column         Non-Null Count  Dtype \n",
            "---  ------         --------------  ----- \n",
            " 0   reviewId       52651 non-null  object\n",
            " 1   userName       52651 non-null  object\n",
            " 2   content        52651 non-null  object\n",
            " 3   score          52651 non-null  int64 \n",
            " 4   thumbsUpCount  52651 non-null  int64 \n",
            " 5   at             52651 non-null  object\n",
            "dtypes: int64(2), object(4)\n",
            "memory usage: 2.4+ MB\n"
          ]
        }
      ],
      "source": [
        "# Load the dataframe\n",
        "df = pd.read_csv(path + '/mobile_legends_reviews.csv')\n",
        "\n",
        "# Basic info\n",
        "print(\"\\n📋 Dataset Info:\")\n",
        "df.info()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dXfnxEs9laEX"
      },
      "source": [
        "## Convert Text to Lowercase"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 556
        },
        "id": "8bb0mc9bk6Ed",
        "outputId": "7c991def-f08d-42c8-e8b3-68698118dc6c"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "                               reviewId       userName  \\\n",
              "0  d9b3706c-a29d-4661-b757-812be9802f33  A Google user   \n",
              "1  cc717825-ea2b-444c-8e80-e4f517314880  A Google user   \n",
              "2  f3c5353d-65f8-486f-81b0-5ac733ed88a0  A Google user   \n",
              "3  5d2611d3-dfda-41b5-87e8-d20498b0f4a4  A Google user   \n",
              "4  38f96ac5-c264-4045-babf-543f576d70b2  A Google user   \n",
              "\n",
              "                                             content  score  thumbsUpCount  \\\n",
              "0  love this game. honestly didn't have any expec...      5              0   \n",
              "1    the graphics? 10/10!!! this game is so funn!! 😄      5              0   \n",
              "2  horrible. your matchmaking sucks. the players ...      1              0   \n",
              "3  dark system. fix your match making, it makes t...      1              0   \n",
              "4  this game has a so much dark sistem and the li...      1              0   \n",
              "\n",
              "                    at  \n",
              "0  2025-06-10 13:20:23  \n",
              "1  2025-06-10 13:19:39  \n",
              "2  2025-06-10 13:18:52  \n",
              "3  2025-06-10 13:15:51  \n",
              "4  2025-06-10 13:11:34  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-cf8be6d2-44de-424a-b960-39dfc465d6d6\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>reviewId</th>\n",
              "      <th>userName</th>\n",
              "      <th>content</th>\n",
              "      <th>score</th>\n",
              "      <th>thumbsUpCount</th>\n",
              "      <th>at</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>d9b3706c-a29d-4661-b757-812be9802f33</td>\n",
              "      <td>A Google user</td>\n",
              "      <td>love this game. honestly didn't have any expec...</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>2025-06-10 13:20:23</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>cc717825-ea2b-444c-8e80-e4f517314880</td>\n",
              "      <td>A Google user</td>\n",
              "      <td>the graphics? 10/10!!! this game is so funn!! 😄</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>2025-06-10 13:19:39</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>f3c5353d-65f8-486f-81b0-5ac733ed88a0</td>\n",
              "      <td>A Google user</td>\n",
              "      <td>horrible. your matchmaking sucks. the players ...</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2025-06-10 13:18:52</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>5d2611d3-dfda-41b5-87e8-d20498b0f4a4</td>\n",
              "      <td>A Google user</td>\n",
              "      <td>dark system. fix your match making, it makes t...</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2025-06-10 13:15:51</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>38f96ac5-c264-4045-babf-543f576d70b2</td>\n",
              "      <td>A Google user</td>\n",
              "      <td>this game has a so much dark sistem and the li...</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2025-06-10 13:11:34</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-cf8be6d2-44de-424a-b960-39dfc465d6d6')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-cf8be6d2-44de-424a-b960-39dfc465d6d6 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-cf8be6d2-44de-424a-b960-39dfc465d6d6');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "    <div id=\"df-d10cff66-ae66-466f-a706-850bf53b5128\">\n",
              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-d10cff66-ae66-466f-a706-850bf53b5128')\"\n",
              "                title=\"Suggest charts\"\n",
              "                style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "      </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "      <script>\n",
              "        async function quickchart(key) {\n",
              "          const quickchartButtonEl =\n",
              "            document.querySelector('#' + key + ' button');\n",
              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "          try {\n",
              "            const charts = await google.colab.kernel.invokeFunction(\n",
              "                'suggestCharts', [key], {});\n",
              "          } catch (error) {\n",
              "            console.error('Error during call to suggestCharts:', error);\n",
              "          }\n",
              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "        }\n",
              "        (() => {\n",
              "          let quickchartButtonEl =\n",
              "            document.querySelector('#df-d10cff66-ae66-466f-a706-850bf53b5128 button');\n",
              "          quickchartButtonEl.style.display =\n",
              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "        })();\n",
              "      </script>\n",
              "    </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"display(df\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"reviewId\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"cc717825-ea2b-444c-8e80-e4f517314880\",\n          \"38f96ac5-c264-4045-babf-543f576d70b2\",\n          \"f3c5353d-65f8-486f-81b0-5ac733ed88a0\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"userName\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"A Google user\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"content\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"the graphics? 10/10!!! this game is so funn!! \\ud83d\\ude04\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2,\n        \"min\": 1,\n        \"max\": 5,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"thumbsUpCount\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"at\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"2025-06-10 13:19:39\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "df['content'] = df['content'].astype(str).str.lower()\n",
        "display(df.head())"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7QS72AhQlsFJ"
      },
      "source": [
        "## Remove Emojis"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 556
        },
        "id": "psXdtHXjlsTL",
        "outputId": "411002ff-c85c-4820-85c3-fbeff6a83d73"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "                               reviewId       userName  \\\n",
              "0  d9b3706c-a29d-4661-b757-812be9802f33  A Google user   \n",
              "1  cc717825-ea2b-444c-8e80-e4f517314880  A Google user   \n",
              "2  f3c5353d-65f8-486f-81b0-5ac733ed88a0  A Google user   \n",
              "3  5d2611d3-dfda-41b5-87e8-d20498b0f4a4  A Google user   \n",
              "4  38f96ac5-c264-4045-babf-543f576d70b2  A Google user   \n",
              "\n",
              "                                             content  score  thumbsUpCount  \\\n",
              "0  love this game. honestly didn't have any expec...      5              0   \n",
              "1     the graphics? 10/10!!! this game is so funn!!       5              0   \n",
              "2  horrible. your matchmaking sucks. the players ...      1              0   \n",
              "3  dark system. fix your match making, it makes t...      1              0   \n",
              "4  this game has a so much dark sistem and the li...      1              0   \n",
              "\n",
              "                    at  \n",
              "0  2025-06-10 13:20:23  \n",
              "1  2025-06-10 13:19:39  \n",
              "2  2025-06-10 13:18:52  \n",
              "3  2025-06-10 13:15:51  \n",
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              "                                                    [key], {});\n",
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              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
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              "      spin 1s steps(1) infinite;\n",
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              "\n",
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              "\n",
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              "          const quickchartButtonEl =\n",
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              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"display(df\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"reviewId\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"cc717825-ea2b-444c-8e80-e4f517314880\",\n          \"38f96ac5-c264-4045-babf-543f576d70b2\",\n          \"f3c5353d-65f8-486f-81b0-5ac733ed88a0\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"userName\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"A Google user\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"content\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"the graphics? 10/10!!! this game is so funn!! \"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2,\n        \"min\": 1,\n        \"max\": 5,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"thumbsUpCount\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"at\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"2025-06-10 13:19:39\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "def remove_emoji(text):\n",
        "    emoji_pattern = re.compile(\"[\"\n",
        "                           u\"\\U0001F600-\\U0001F64F\"\n",
        "                           u\"\\U0001F300-\\U0001F5FF\"\n",
        "                           u\"\\U0001F680-\\U0001F6FF\"\n",
        "                           u\"\\U0001F1E0-\\U0001F1FF\"\n",
        "                           \"]+\", flags=re.UNICODE)\n",
        "    return emoji_pattern.sub(r'', text)\n",
        "\n",
        "df['content'] = df['content'].apply(remove_emoji)\n",
        "display(df.head())"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oO2POom8mX1u"
      },
      "source": [
        "## Remove Stop Words"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 574
        },
        "id": "2-mbjSPomX8D",
        "outputId": "6face926-453e-47cf-e4cf-b07ebc54a952"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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              "                                             content  \\\n",
              "0  love this game. honestly didn't have any expec...   \n",
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              "2  horrible. your matchmaking sucks. the players ...   \n",
              "3  dark system. fix your match making, it makes t...   \n",
              "4  this game has a so much dark sistem and the li...   \n",
              "\n",
              "                                             cleaned  \n",
              "0  love game. honestly expectations going knew hu...  \n",
              "1                     graphics? 10/10!!! game funn!!  \n",
              "2  horrible. matchmaking sucks. players either re...  \n",
              "3  dark system. fix match making, makes game enjo...  \n",
              "4  game much dark sistem line always bad lost acc...  "
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              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-c7137f48-fdd7-43d3-b16e-40fc5e9a566e');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "    <div id=\"df-8d97dd4a-151f-4456-b8c6-846d21fa0ad8\">\n",
              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-8d97dd4a-151f-4456-b8c6-846d21fa0ad8')\"\n",
              "                title=\"Suggest charts\"\n",
              "                style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "      </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "      <script>\n",
              "        async function quickchart(key) {\n",
              "          const quickchartButtonEl =\n",
              "            document.querySelector('#' + key + ' button');\n",
              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "          try {\n",
              "            const charts = await google.colab.kernel.invokeFunction(\n",
              "                'suggestCharts', [key], {});\n",
              "          } catch (error) {\n",
              "            console.error('Error during call to suggestCharts:', error);\n",
              "          }\n",
              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "        }\n",
              "        (() => {\n",
              "          let quickchartButtonEl =\n",
              "            document.querySelector('#df-8d97dd4a-151f-4456-b8c6-846d21fa0ad8 button');\n",
              "          quickchartButtonEl.style.display =\n",
              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "        })();\n",
              "      </script>\n",
              "    </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"display(df[['content', 'cleaned']]\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"content\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"the graphics? 10/10!!! this game is so funn!! \",\n          \"this game has a so much dark sistem and the line up is always bad and when i lost my account i went to ask in the customer service using mail but the moontoon inbox is always full so now i cant retrieve my old account back.i bought so much money for diamonds and now this is what i get.\",\n          \"horrible. your matchmaking sucks. the players are either really good or really bad. you prioritize skin more than skills. matchmaking in rank are absolutely horrible, you put terrible performing players with another terrible performing players. 100% would not recommend. if you want stress then download this game. and its super laggy even if my network is fine. just absolutely horrible. fix it.\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"cleaned\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"graphics? 10/10!!! game funn!!\",\n          \"game much dark sistem line always bad lost account went ask customer service using mail moontoon inbox always full cant retrieve old account back.i bought much money diamonds get.\",\n          \"horrible. matchmaking sucks. players either really good really bad. prioritize skin skills. matchmaking rank absolutely horrible, put terrible performing players another terrible performing players. 100% would recommend. want stress download game. super laggy even network fine. absolutely horrible. fix it.\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "stop_words = set(stopwords.words('english'))\n",
        "\n",
        "df['cleaned'] = df['content'].apply(lambda x: ' '.join(\n",
        "    [word for word in x.split() if word not in stop_words]\n",
        "))\n",
        "display(df[['content', 'cleaned']].head())"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FIyD2PvAm0Xa"
      },
      "source": [
        "## Tokenization"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 556
        },
        "collapsed": true,
        "id": "FmvqmMQqm0ex",
        "outputId": "c029831b-7264-4a6c-b8dd-cc40953f1d5e"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "                                             cleaned  \\\n",
              "0  love game. honestly expectations going knew hu...   \n",
              "1                     graphics? 10/10!!! game funn!!   \n",
              "2  horrible. matchmaking sucks. players either re...   \n",
              "3  dark system. fix match making, makes game enjo...   \n",
              "4  game much dark sistem line always bad lost acc...   \n",
              "\n",
              "                                              tokens  \n",
              "0  [love, game, ., honestly, expectations, going,...  \n",
              "1    [graphics, ?, 10/10, !, !, !, game, funn, !, !]  \n",
              "2  [horrible, ., matchmaking, sucks, ., players, ...  \n",
              "3  [dark, system, ., fix, match, making, ,, makes...  \n",
              "4  [game, much, dark, sistem, line, always, bad, ...  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-c00223d4-d15c-4318-aaf4-033895386d31\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>cleaned</th>\n",
              "      <th>tokens</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>love game. honestly expectations going knew hu...</td>\n",
              "      <td>[love, game, ., honestly, expectations, going,...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>graphics? 10/10!!! game funn!!</td>\n",
              "      <td>[graphics, ?, 10/10, !, !, !, game, funn, !, !]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>horrible. matchmaking sucks. players either re...</td>\n",
              "      <td>[horrible, ., matchmaking, sucks, ., players, ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>dark system. fix match making, makes game enjo...</td>\n",
              "      <td>[dark, system, ., fix, match, making, ,, makes...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>game much dark sistem line always bad lost acc...</td>\n",
              "      <td>[game, much, dark, sistem, line, always, bad, ...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c00223d4-d15c-4318-aaf4-033895386d31')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-c00223d4-d15c-4318-aaf4-033895386d31 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-c00223d4-d15c-4318-aaf4-033895386d31');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "    <div id=\"df-fb57f65c-8fab-4e34-801f-34b329f6ea5c\">\n",
              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-fb57f65c-8fab-4e34-801f-34b329f6ea5c')\"\n",
              "                title=\"Suggest charts\"\n",
              "                style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "      </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "      <script>\n",
              "        async function quickchart(key) {\n",
              "          const quickchartButtonEl =\n",
              "            document.querySelector('#' + key + ' button');\n",
              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "          try {\n",
              "            const charts = await google.colab.kernel.invokeFunction(\n",
              "                'suggestCharts', [key], {});\n",
              "          } catch (error) {\n",
              "            console.error('Error during call to suggestCharts:', error);\n",
              "          }\n",
              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "        }\n",
              "        (() => {\n",
              "          let quickchartButtonEl =\n",
              "            document.querySelector('#df-fb57f65c-8fab-4e34-801f-34b329f6ea5c button');\n",
              "          quickchartButtonEl.style.display =\n",
              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "        })();\n",
              "      </script>\n",
              "    </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"display(df[['cleaned', 'tokens']]\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"cleaned\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"graphics? 10/10!!! game funn!!\",\n          \"game much dark sistem line always bad lost account went ask customer service using mail moontoon inbox always full cant retrieve old account back.i bought much money diamonds get.\",\n          \"horrible. matchmaking sucks. players either really good really bad. prioritize skin skills. matchmaking rank absolutely horrible, put terrible performing players another terrible performing players. 100% would recommend. want stress download game. super laggy even network fine. absolutely horrible. fix it.\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"tokens\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "df['tokens'] = df['cleaned'].apply(word_tokenize)\n",
        "display(df[['cleaned', 'tokens']].head())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "collapsed": true,
        "id": "5085d4c9",
        "outputId": "3b712015-9649-41b0-cceb-76c2d5b525d1"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "                                              tokens  \\\n",
              "0  [love, game, ., honestly, expectations, going,...   \n",
              "1    [graphics, ?, 10/10, !, !, !, game, funn, !, !]   \n",
              "2  [horrible, ., matchmaking, sucks, ., players, ...   \n",
              "3  [dark, system, ., fix, match, making, ,, makes...   \n",
              "4  [game, much, dark, sistem, line, always, bad, ...   \n",
              "\n",
              "                                   lemmatized_tokens  \n",
              "0  [love, game, ., honestly, expectation, going, ...  \n",
              "1     [graphic, ?, 10/10, !, !, !, game, funn, !, !]  \n",
              "2  [horrible, ., matchmaking, suck, ., player, ei...  \n",
              "3  [dark, system, ., fix, match, making, ,, make,...  \n",
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              "      <th>lemmatized_tokens</th>\n",
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              "  </thead>\n",
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              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>[love, game, ., honestly, expectations, going,...</td>\n",
              "      <td>[love, game, ., honestly, expectation, going, ...</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>[graphics, ?, 10/10, !, !, !, game, funn, !, !]</td>\n",
              "      <td>[graphic, ?, 10/10, !, !, !, game, funn, !, !]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>[horrible, ., matchmaking, sucks, ., players, ...</td>\n",
              "      <td>[horrible, ., matchmaking, suck, ., player, ei...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
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              "      <td>[dark, system, ., fix, match, making, ,, make,...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>[game, much, dark, sistem, line, always, bad, ...</td>\n",
              "      <td>[game, much, dark, sistem, line, always, bad, ...</td>\n",
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              "    [theme=dark] .colab-df-convert {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "\n",
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              "        document.querySelector('#df-1abf48ba-0262-4a2e-9516-c0432c53bbe9 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-1abf48ba-0262-4a2e-9516-c0432c53bbe9');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "    </g>\n",
              "</svg>\n",
              "      </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "      <script>\n",
              "        async function quickchart(key) {\n",
              "          const quickchartButtonEl =\n",
              "            document.querySelector('#' + key + ' button');\n",
              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "          try {\n",
              "            const charts = await google.colab.kernel.invokeFunction(\n",
              "                'suggestCharts', [key], {});\n",
              "          } catch (error) {\n",
              "            console.error('Error during call to suggestCharts:', error);\n",
              "          }\n",
              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "        }\n",
              "        (() => {\n",
              "          let quickchartButtonEl =\n",
              "            document.querySelector('#df-2d1d6655-244e-470d-95b0-14294c6b898c button');\n",
              "          quickchartButtonEl.style.display =\n",
              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "        })();\n",
              "      </script>\n",
              "    </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"display(df[['tokens', 'lemmatized_tokens']]\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"tokens\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"lemmatized_tokens\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "from nltk.stem import WordNetLemmatizer\n",
        "\n",
        "lemmatizer = WordNetLemmatizer()\n",
        "\n",
        "def lemmatize_tokens(tokens):\n",
        "    return [lemmatizer.lemmatize(token) for token in tokens]\n",
        "\n",
        "df['lemmatized_tokens'] = df['tokens'].apply(lemmatize_tokens)\n",
        "display(df[['tokens', 'lemmatized_tokens']].head())"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qg9ghLzp7dsz"
      },
      "source": [
        "## Remove Duplicate"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 574
        },
        "collapsed": true,
        "id": "mxYAP6hc7i2d",
        "outputId": "a803794c-0e13-4249-c56b-e36386dde223"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "                               reviewId       userName  \\\n",
              "0  d9b3706c-a29d-4661-b757-812be9802f33  A Google user   \n",
              "1  cc717825-ea2b-444c-8e80-e4f517314880  A Google user   \n",
              "2  f3c5353d-65f8-486f-81b0-5ac733ed88a0  A Google user   \n",
              "3  5d2611d3-dfda-41b5-87e8-d20498b0f4a4  A Google user   \n",
              "4  38f96ac5-c264-4045-babf-543f576d70b2  A Google user   \n",
              "\n",
              "                                             content  score  thumbsUpCount  \\\n",
              "0  love this game. honestly didn't have any expec...      5              0   \n",
              "1     the graphics? 10/10!!! this game is so funn!!       5              0   \n",
              "2  horrible. your matchmaking sucks. the players ...      1              0   \n",
              "3  dark system. fix your match making, it makes t...      1              0   \n",
              "4  this game has a so much dark sistem and the li...      1              0   \n",
              "\n",
              "                    at                                            cleaned  \\\n",
              "0  2025-06-10 13:20:23  love game. honestly expectations going knew hu...   \n",
              "1  2025-06-10 13:19:39                     graphics? 10/10!!! game funn!!   \n",
              "2  2025-06-10 13:18:52  horrible. matchmaking sucks. players either re...   \n",
              "3  2025-06-10 13:15:51  dark system. fix match making, makes game enjo...   \n",
              "4  2025-06-10 13:11:34  game much dark sistem line always bad lost acc...   \n",
              "\n",
              "                                              tokens  \\\n",
              "0  [love, game, ., honestly, expectations, going,...   \n",
              "1    [graphics, ?, 10/10, !, !, !, game, funn, !, !]   \n",
              "2  [horrible, ., matchmaking, sucks, ., players, ...   \n",
              "3  [dark, system, ., fix, match, making, ,, makes...   \n",
              "4  [game, much, dark, sistem, line, always, bad, ...   \n",
              "\n",
              "                                   lemmatized_tokens  \n",
              "0  [love, game, ., honestly, expectation, going, ...  \n",
              "1     [graphic, ?, 10/10, !, !, !, game, funn, !, !]  \n",
              "2  [horrible, ., matchmaking, suck, ., player, ei...  \n",
              "3  [dark, system, ., fix, match, making, ,, make,...  \n",
              "4  [game, much, dark, sistem, line, always, bad, ...  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-e7417718-98a5-47fe-80f4-239fc6de3159\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>reviewId</th>\n",
              "      <th>userName</th>\n",
              "      <th>content</th>\n",
              "      <th>score</th>\n",
              "      <th>thumbsUpCount</th>\n",
              "      <th>at</th>\n",
              "      <th>cleaned</th>\n",
              "      <th>tokens</th>\n",
              "      <th>lemmatized_tokens</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>d9b3706c-a29d-4661-b757-812be9802f33</td>\n",
              "      <td>A Google user</td>\n",
              "      <td>love this game. honestly didn't have any expec...</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>2025-06-10 13:20:23</td>\n",
              "      <td>love game. honestly expectations going knew hu...</td>\n",
              "      <td>[love, game, ., honestly, expectations, going,...</td>\n",
              "      <td>[love, game, ., honestly, expectation, going, ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>cc717825-ea2b-444c-8e80-e4f517314880</td>\n",
              "      <td>A Google user</td>\n",
              "      <td>the graphics? 10/10!!! this game is so funn!!</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>2025-06-10 13:19:39</td>\n",
              "      <td>graphics? 10/10!!! game funn!!</td>\n",
              "      <td>[graphics, ?, 10/10, !, !, !, game, funn, !, !]</td>\n",
              "      <td>[graphic, ?, 10/10, !, !, !, game, funn, !, !]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>f3c5353d-65f8-486f-81b0-5ac733ed88a0</td>\n",
              "      <td>A Google user</td>\n",
              "      <td>horrible. your matchmaking sucks. the players ...</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2025-06-10 13:18:52</td>\n",
              "      <td>horrible. matchmaking sucks. players either re...</td>\n",
              "      <td>[horrible, ., matchmaking, sucks, ., players, ...</td>\n",
              "      <td>[horrible, ., matchmaking, suck, ., player, ei...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>5d2611d3-dfda-41b5-87e8-d20498b0f4a4</td>\n",
              "      <td>A Google user</td>\n",
              "      <td>dark system. fix your match making, it makes t...</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2025-06-10 13:15:51</td>\n",
              "      <td>dark system. fix match making, makes game enjo...</td>\n",
              "      <td>[dark, system, ., fix, match, making, ,, makes...</td>\n",
              "      <td>[dark, system, ., fix, match, making, ,, make,...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>38f96ac5-c264-4045-babf-543f576d70b2</td>\n",
              "      <td>A Google user</td>\n",
              "      <td>this game has a so much dark sistem and the li...</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2025-06-10 13:11:34</td>\n",
              "      <td>game much dark sistem line always bad lost acc...</td>\n",
              "      <td>[game, much, dark, sistem, line, always, bad, ...</td>\n",
              "      <td>[game, much, dark, sistem, line, always, bad, ...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e7417718-98a5-47fe-80f4-239fc6de3159')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-e7417718-98a5-47fe-80f4-239fc6de3159 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-e7417718-98a5-47fe-80f4-239fc6de3159');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "    <div id=\"df-dea52736-e349-4261-b04c-32b58405faf3\">\n",
              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-dea52736-e349-4261-b04c-32b58405faf3')\"\n",
              "                title=\"Suggest charts\"\n",
              "                style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "      </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "      <script>\n",
              "        async function quickchart(key) {\n",
              "          const quickchartButtonEl =\n",
              "            document.querySelector('#' + key + ' button');\n",
              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "          try {\n",
              "            const charts = await google.colab.kernel.invokeFunction(\n",
              "                'suggestCharts', [key], {});\n",
              "          } catch (error) {\n",
              "            console.error('Error during call to suggestCharts:', error);\n",
              "          }\n",
              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "        }\n",
              "        (() => {\n",
              "          let quickchartButtonEl =\n",
              "            document.querySelector('#df-dea52736-e349-4261-b04c-32b58405faf3 button');\n",
              "          quickchartButtonEl.style.display =\n",
              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "        })();\n",
              "      </script>\n",
              "    </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"display(df\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"reviewId\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"cc717825-ea2b-444c-8e80-e4f517314880\",\n          \"38f96ac5-c264-4045-babf-543f576d70b2\",\n          \"f3c5353d-65f8-486f-81b0-5ac733ed88a0\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"userName\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"A Google user\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"content\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"the graphics? 10/10!!! this game is so funn!! \"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2,\n        \"min\": 1,\n        \"max\": 5,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"thumbsUpCount\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"at\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"2025-06-10 13:19:39\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"cleaned\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"graphics? 10/10!!! game funn!!\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"tokens\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"lemmatized_tokens\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "df.drop_duplicates(subset=['content'], inplace=True)\n",
        "display(df.head())"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IXyZNaWu9Bpf"
      },
      "source": [
        "## Handle missing values"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 440
        },
        "collapsed": true,
        "id": "vOSezUDr9BH2",
        "outputId": "aaf6671f-f872-446c-bb69-35689e73f442"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "📊 Missing Values:\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "reviewId             0\n",
              "userName             0\n",
              "content              0\n",
              "score                0\n",
              "thumbsUpCount        0\n",
              "at                   0\n",
              "cleaned              0\n",
              "tokens               0\n",
              "lemmatized_tokens    0\n",
              "dtype: int64"
            ],
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>0</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>reviewId</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>userName</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>content</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>score</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>thumbsUpCount</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>at</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>cleaned</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>tokens</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>lemmatized_tokens</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "print(\"\\n📊 Missing Values:\")\n",
        "display(df.isnull().sum())"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NM9rWDZD9s_X"
      },
      "source": [
        "## Remove special characters"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 574
        },
        "collapsed": true,
        "id": "o6UvGRHo9z0O",
        "outputId": "87e41095-6823-4dd8-88ae-8e5494a94514"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "                               reviewId       userName  \\\n",
              "0  d9b3706c-a29d-4661-b757-812be9802f33  A Google user   \n",
              "1  cc717825-ea2b-444c-8e80-e4f517314880  A Google user   \n",
              "2  f3c5353d-65f8-486f-81b0-5ac733ed88a0  A Google user   \n",
              "3  5d2611d3-dfda-41b5-87e8-d20498b0f4a4  A Google user   \n",
              "4  38f96ac5-c264-4045-babf-543f576d70b2  A Google user   \n",
              "\n",
              "                                             content  score  thumbsUpCount  \\\n",
              "0  love this game. honestly didnt have any expect...      5              0   \n",
              "1      the graphics? 1010!!! this game is so funn!!       5              0   \n",
              "2  horrible. your matchmaking sucks. the players ...      1              0   \n",
              "3  dark system. fix your match making, it makes t...      1              0   \n",
              "4  this game has a so much dark sistem and the li...      1              0   \n",
              "\n",
              "                    at                                            cleaned  \\\n",
              "0  2025-06-10 13:20:23  love game. honestly expectations going knew hu...   \n",
              "1  2025-06-10 13:19:39                     graphics? 10/10!!! game funn!!   \n",
              "2  2025-06-10 13:18:52  horrible. matchmaking sucks. players either re...   \n",
              "3  2025-06-10 13:15:51  dark system. fix match making, makes game enjo...   \n",
              "4  2025-06-10 13:11:34  game much dark sistem line always bad lost acc...   \n",
              "\n",
              "                                              tokens  \\\n",
              "0  [love, game, ., honestly, expectations, going,...   \n",
              "1    [graphics, ?, 10/10, !, !, !, game, funn, !, !]   \n",
              "2  [horrible, ., matchmaking, sucks, ., players, ...   \n",
              "3  [dark, system, ., fix, match, making, ,, makes...   \n",
              "4  [game, much, dark, sistem, line, always, bad, ...   \n",
              "\n",
              "                                   lemmatized_tokens  \n",
              "0  [love, game, ., honestly, expectation, going, ...  \n",
              "1     [graphic, ?, 10/10, !, !, !, game, funn, !, !]  \n",
              "2  [horrible, ., matchmaking, suck, ., player, ei...  \n",
              "3  [dark, system, ., fix, match, making, ,, make,...  \n",
              "4  [game, much, dark, sistem, line, always, bad, ...  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-f44de984-004c-4207-98c6-3ec61a70de0b\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>reviewId</th>\n",
              "      <th>userName</th>\n",
              "      <th>content</th>\n",
              "      <th>score</th>\n",
              "      <th>thumbsUpCount</th>\n",
              "      <th>at</th>\n",
              "      <th>cleaned</th>\n",
              "      <th>tokens</th>\n",
              "      <th>lemmatized_tokens</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>d9b3706c-a29d-4661-b757-812be9802f33</td>\n",
              "      <td>A Google user</td>\n",
              "      <td>love this game. honestly didnt have any expect...</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>2025-06-10 13:20:23</td>\n",
              "      <td>love game. honestly expectations going knew hu...</td>\n",
              "      <td>[love, game, ., honestly, expectations, going,...</td>\n",
              "      <td>[love, game, ., honestly, expectation, going, ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>cc717825-ea2b-444c-8e80-e4f517314880</td>\n",
              "      <td>A Google user</td>\n",
              "      <td>the graphics? 1010!!! this game is so funn!!</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>2025-06-10 13:19:39</td>\n",
              "      <td>graphics? 10/10!!! game funn!!</td>\n",
              "      <td>[graphics, ?, 10/10, !, !, !, game, funn, !, !]</td>\n",
              "      <td>[graphic, ?, 10/10, !, !, !, game, funn, !, !]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>f3c5353d-65f8-486f-81b0-5ac733ed88a0</td>\n",
              "      <td>A Google user</td>\n",
              "      <td>horrible. your matchmaking sucks. the players ...</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2025-06-10 13:18:52</td>\n",
              "      <td>horrible. matchmaking sucks. players either re...</td>\n",
              "      <td>[horrible, ., matchmaking, sucks, ., players, ...</td>\n",
              "      <td>[horrible, ., matchmaking, suck, ., player, ei...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>5d2611d3-dfda-41b5-87e8-d20498b0f4a4</td>\n",
              "      <td>A Google user</td>\n",
              "      <td>dark system. fix your match making, it makes t...</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2025-06-10 13:15:51</td>\n",
              "      <td>dark system. fix match making, makes game enjo...</td>\n",
              "      <td>[dark, system, ., fix, match, making, ,, makes...</td>\n",
              "      <td>[dark, system, ., fix, match, making, ,, make,...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>38f96ac5-c264-4045-babf-543f576d70b2</td>\n",
              "      <td>A Google user</td>\n",
              "      <td>this game has a so much dark sistem and the li...</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2025-06-10 13:11:34</td>\n",
              "      <td>game much dark sistem line always bad lost acc...</td>\n",
              "      <td>[game, much, dark, sistem, line, always, bad, ...</td>\n",
              "      <td>[game, much, dark, sistem, line, always, bad, ...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f44de984-004c-4207-98c6-3ec61a70de0b')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-f44de984-004c-4207-98c6-3ec61a70de0b button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-f44de984-004c-4207-98c6-3ec61a70de0b');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "    <div id=\"df-4cdba2d6-c155-4303-abb4-0033141cb065\">\n",
              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-4cdba2d6-c155-4303-abb4-0033141cb065')\"\n",
              "                title=\"Suggest charts\"\n",
              "                style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "      </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "      <script>\n",
              "        async function quickchart(key) {\n",
              "          const quickchartButtonEl =\n",
              "            document.querySelector('#' + key + ' button');\n",
              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "          try {\n",
              "            const charts = await google.colab.kernel.invokeFunction(\n",
              "                'suggestCharts', [key], {});\n",
              "          } catch (error) {\n",
              "            console.error('Error during call to suggestCharts:', error);\n",
              "          }\n",
              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "        }\n",
              "        (() => {\n",
              "          let quickchartButtonEl =\n",
              "            document.querySelector('#df-4cdba2d6-c155-4303-abb4-0033141cb065 button');\n",
              "          quickchartButtonEl.style.display =\n",
              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "        })();\n",
              "      </script>\n",
              "    </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"display(df\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"reviewId\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"cc717825-ea2b-444c-8e80-e4f517314880\",\n          \"38f96ac5-c264-4045-babf-543f576d70b2\",\n          \"f3c5353d-65f8-486f-81b0-5ac733ed88a0\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"userName\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"A Google user\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"content\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"the graphics? 1010!!! this game is so funn!! \"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2,\n        \"min\": 1,\n        \"max\": 5,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"thumbsUpCount\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"at\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"2025-06-10 13:19:39\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"cleaned\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"graphics? 10/10!!! game funn!!\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"tokens\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"lemmatized_tokens\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "df['content'] = df['content'].apply(lambda x: re.sub(r'[^a-zA-Z0-9\\s.,!?;:]', '', x))\n",
        "display(df.head())"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mlqrAVvX-6gb"
      },
      "source": [
        "## Remove extra whitespace"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 574
        },
        "collapsed": true,
        "id": "wGowoSgc-6sn",
        "outputId": "2369fcac-29bf-4f47-c0a6-0ab2d5666c59"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "                               reviewId       userName  \\\n",
              "0  d9b3706c-a29d-4661-b757-812be9802f33  A Google user   \n",
              "1  cc717825-ea2b-444c-8e80-e4f517314880  A Google user   \n",
              "2  f3c5353d-65f8-486f-81b0-5ac733ed88a0  A Google user   \n",
              "3  5d2611d3-dfda-41b5-87e8-d20498b0f4a4  A Google user   \n",
              "4  38f96ac5-c264-4045-babf-543f576d70b2  A Google user   \n",
              "\n",
              "                                             content  score  thumbsUpCount  \\\n",
              "0  love this game. honestly didnt have any expect...      5              0   \n",
              "1       the graphics? 1010!!! this game is so funn!!      5              0   \n",
              "2  horrible. your matchmaking sucks. the players ...      1              0   \n",
              "3  dark system. fix your match making, it makes t...      1              0   \n",
              "4  this game has a so much dark sistem and the li...      1              0   \n",
              "\n",
              "                    at                                            cleaned  \\\n",
              "0  2025-06-10 13:20:23  love game. honestly expectations going knew hu...   \n",
              "1  2025-06-10 13:19:39                     graphics? 10/10!!! game funn!!   \n",
              "2  2025-06-10 13:18:52  horrible. matchmaking sucks. players either re...   \n",
              "3  2025-06-10 13:15:51  dark system. fix match making, makes game enjo...   \n",
              "4  2025-06-10 13:11:34  game much dark sistem line always bad lost acc...   \n",
              "\n",
              "                                              tokens  \\\n",
              "0  [love, game, ., honestly, expectations, going,...   \n",
              "1    [graphics, ?, 10/10, !, !, !, game, funn, !, !]   \n",
              "2  [horrible, ., matchmaking, sucks, ., players, ...   \n",
              "3  [dark, system, ., fix, match, making, ,, makes...   \n",
              "4  [game, much, dark, sistem, line, always, bad, ...   \n",
              "\n",
              "                                   lemmatized_tokens  \n",
              "0  [love, game, ., honestly, expectation, going, ...  \n",
              "1     [graphic, ?, 10/10, !, !, !, game, funn, !, !]  \n",
              "2  [horrible, ., matchmaking, suck, ., player, ei...  \n",
              "3  [dark, system, ., fix, match, making, ,, make,...  \n",
              "4  [game, much, dark, sistem, line, always, bad, ...  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-4ebdf694-ea0b-4a03-9a24-575f0703a4c6\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>reviewId</th>\n",
              "      <th>userName</th>\n",
              "      <th>content</th>\n",
              "      <th>score</th>\n",
              "      <th>thumbsUpCount</th>\n",
              "      <th>at</th>\n",
              "      <th>cleaned</th>\n",
              "      <th>tokens</th>\n",
              "      <th>lemmatized_tokens</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>d9b3706c-a29d-4661-b757-812be9802f33</td>\n",
              "      <td>A Google user</td>\n",
              "      <td>love this game. honestly didnt have any expect...</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>2025-06-10 13:20:23</td>\n",
              "      <td>love game. honestly expectations going knew hu...</td>\n",
              "      <td>[love, game, ., honestly, expectations, going,...</td>\n",
              "      <td>[love, game, ., honestly, expectation, going, ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>cc717825-ea2b-444c-8e80-e4f517314880</td>\n",
              "      <td>A Google user</td>\n",
              "      <td>the graphics? 1010!!! this game is so funn!!</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>2025-06-10 13:19:39</td>\n",
              "      <td>graphics? 10/10!!! game funn!!</td>\n",
              "      <td>[graphics, ?, 10/10, !, !, !, game, funn, !, !]</td>\n",
              "      <td>[graphic, ?, 10/10, !, !, !, game, funn, !, !]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>f3c5353d-65f8-486f-81b0-5ac733ed88a0</td>\n",
              "      <td>A Google user</td>\n",
              "      <td>horrible. your matchmaking sucks. the players ...</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2025-06-10 13:18:52</td>\n",
              "      <td>horrible. matchmaking sucks. players either re...</td>\n",
              "      <td>[horrible, ., matchmaking, sucks, ., players, ...</td>\n",
              "      <td>[horrible, ., matchmaking, suck, ., player, ei...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>5d2611d3-dfda-41b5-87e8-d20498b0f4a4</td>\n",
              "      <td>A Google user</td>\n",
              "      <td>dark system. fix your match making, it makes t...</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2025-06-10 13:15:51</td>\n",
              "      <td>dark system. fix match making, makes game enjo...</td>\n",
              "      <td>[dark, system, ., fix, match, making, ,, makes...</td>\n",
              "      <td>[dark, system, ., fix, match, making, ,, make,...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>38f96ac5-c264-4045-babf-543f576d70b2</td>\n",
              "      <td>A Google user</td>\n",
              "      <td>this game has a so much dark sistem and the li...</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2025-06-10 13:11:34</td>\n",
              "      <td>game much dark sistem line always bad lost acc...</td>\n",
              "      <td>[game, much, dark, sistem, line, always, bad, ...</td>\n",
              "      <td>[game, much, dark, sistem, line, always, bad, ...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-4ebdf694-ea0b-4a03-9a24-575f0703a4c6')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-4ebdf694-ea0b-4a03-9a24-575f0703a4c6 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-4ebdf694-ea0b-4a03-9a24-575f0703a4c6');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "    <div id=\"df-9dd713ae-67e1-46f9-b029-edf1b9853491\">\n",
              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-9dd713ae-67e1-46f9-b029-edf1b9853491')\"\n",
              "                title=\"Suggest charts\"\n",
              "                style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "      </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "      <script>\n",
              "        async function quickchart(key) {\n",
              "          const quickchartButtonEl =\n",
              "            document.querySelector('#' + key + ' button');\n",
              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "          try {\n",
              "            const charts = await google.colab.kernel.invokeFunction(\n",
              "                'suggestCharts', [key], {});\n",
              "          } catch (error) {\n",
              "            console.error('Error during call to suggestCharts:', error);\n",
              "          }\n",
              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "        }\n",
              "        (() => {\n",
              "          let quickchartButtonEl =\n",
              "            document.querySelector('#df-9dd713ae-67e1-46f9-b029-edf1b9853491 button');\n",
              "          quickchartButtonEl.style.display =\n",
              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "        })();\n",
              "      </script>\n",
              "    </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"display(df\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"reviewId\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"cc717825-ea2b-444c-8e80-e4f517314880\",\n          \"38f96ac5-c264-4045-babf-543f576d70b2\",\n          \"f3c5353d-65f8-486f-81b0-5ac733ed88a0\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"userName\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"A Google user\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"content\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"the graphics? 1010!!! this game is so funn!!\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2,\n        \"min\": 1,\n        \"max\": 5,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"thumbsUpCount\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"at\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"2025-06-10 13:19:39\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"cleaned\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"graphics? 10/10!!! game funn!!\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"tokens\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"lemmatized_tokens\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "df['content'] = df['content'].str.replace(r'\\s+', ' ', regex=True).str.strip()\n",
        "display(df.head())"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CHFH84cv_cn3"
      },
      "source": [
        "## Spelling correction"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4sdMD3Q4_kPA",
        "outputId": "91ed9b83-5ea1-4c27-ef5e-dcc471456e48"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting autocorrect\n",
            "  Downloading autocorrect-2.6.1.tar.gz (622 kB)\n",
            "\u001b[?25l     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/622.8 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m622.8/622.8 kB\u001b[0m \u001b[31m35.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Building wheels for collected packages: autocorrect\n",
            "  Building wheel for autocorrect (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for autocorrect: filename=autocorrect-2.6.1-py3-none-any.whl size=622364 sha256=6a702f9f97205b7540575c197cc2607a2b34fb81962f23f0a6d93686e29311f6\n",
            "  Stored in directory: /root/.cache/pip/wheels/b6/28/c2/9ddf8f57f871b55b6fd0ab99c887531fb9a66e5ff236b82aee\n",
            "Successfully built autocorrect\n",
            "Installing collected packages: autocorrect\n",
            "Successfully installed autocorrect-2.6.1\n"
          ]
        }
      ],
      "source": [
        "!pip install autocorrect\n",
        "from autocorrect import Speller\n",
        "spell = Speller(lang='en')\n",
        "\n",
        "def correct_spelling_fast(text):\n",
        "    corrected = \" \".join([spell(word) for word in text.split()])\n",
        "    return corrected"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GtbFqiTh_78_"
      },
      "source": [
        "## Handle Negations"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "_8886mWFGriD"
      },
      "outputs": [],
      "source": [
        "def handle_negations(text):\n",
        "    negation_words = [\"not\", \"no\", \"never\", \"n't\"]\n",
        "    punctuations = {\".\", \"!\", \"?\", \",\", \";\", \":\"}\n",
        "\n",
        "    tokens = text.split()\n",
        "    new_tokens = []\n",
        "    negate = False\n",
        "\n",
        "    for token in tokens:\n",
        "        lower = token.lower()\n",
        "\n",
        "        # Start negation\n",
        "        if any(lower.startswith(n) for n in negation_words):\n",
        "            negate = True\n",
        "            new_tokens.append(token)\n",
        "\n",
        "        # Apply negation until punctuation\n",
        "        elif negate:\n",
        "            if token in punctuations:\n",
        "                negate = False\n",
        "                new_tokens.append(token)\n",
        "            else:\n",
        "                new_tokens.append(token + \"_NEG\")\n",
        "        else:\n",
        "            new_tokens.append(token)\n",
        "\n",
        "        # Stop on punctuation\n",
        "        if token in punctuations:\n",
        "            negate = False\n",
        "\n",
        "    return \" \".join(new_tokens)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xriNR-iRst_x"
      },
      "source": [
        "## Data Labeling"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "v6UpbRDssuHW"
      },
      "outputs": [],
      "source": [
        "\n",
        "# 1) DROP 3-star reviews\n",
        "df = df[df[\"score\"] != 3].reset_index(drop=True)\n",
        "\n",
        "# 2) relabel sentiment\n",
        "def label_sentiment(rating):\n",
        "    if rating in [1, 2]:\n",
        "        return \"negative\"\n",
        "    elif rating in [4, 5]:\n",
        "        return \"positive\"\n",
        "\n",
        "df[\"sentiment\"] = df[\"score\"].apply(label_sentiment)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DcQok1Zh68Cu"
      },
      "source": [
        "## Word Cloud"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        },
        "id": "zds_PwQH8LqC",
        "outputId": "6bceae0c-3841-41b1-e23c-b73553a3ae22"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Please execute all the cells above this one before running this cell.\n",
        "text = \" \".join(df['cleaned'])\n",
        "wordcloud = WordCloud(width=800, height=400, background_color='white').generate(text)\n",
        "\n",
        "plt.figure(figsize=(10,5))\n",
        "plt.imshow(wordcloud, interpolation='bilinear')\n",
        "plt.axis('off')\n",
        "plt.title(\"Most Frequent Words in Reviews\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "inAwrxmV446S"
      },
      "source": [
        "## Visualization (Bar & Pie Charts)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 90
        },
        "id": "CViobZ6KC5UU",
        "outputId": "1517aa0f-6c5f-42e9-e21d-dd8854a9ce64"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "✅ Sentiment column created.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "import seaborn as sns # Added import statement\n",
        "# Please execute all the cells above this one before running this cell.\n",
        "# Data Labeling (Sentiment) - Re-adding this step\n",
        "def label_sentiment(rating):\n",
        "    if rating <= 3:\n",
        "        return 'negative'\n",
        "\n",
        "    else:\n",
        "        return 'positive'\n",
        "\n",
        "df['sentiment'] = df['score'].apply(label_sentiment)\n",
        "print(\"✅ Sentiment column created.\")\n",
        "\n",
        "\n",
        "\n",
        "# Bar chart\n",
        "sns.countplot(data=df, x='sentiment', hue='sentiment', palette='coolwarm', legend=False) # Modified to address FutureWarning\n",
        "plt.title('Sentiment Distribution')\n",
        "plt.show()\n",
        "\n",
        "# Pie chart\n",
        "plt.figure(figsize=(5,5)) # Create a new figure for the pie chart\n",
        "df['sentiment'].value_counts().plot.pie(autopct='%1.1f%%',\n",
        "                                        colors=['red', 'gold', 'green'])\n",
        "plt.title(\"Sentiment Percentage\")\n",
        "plt.ylabel('')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1HQj2U5kDhpC"
      },
      "source": [
        "## Gradient Search (Hyperparameter Tuning)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AtlCijuuGohx",
        "outputId": "5496c83b-6a95-4849-dc5d-d6a57c6688b2"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "🚀 Starting Grid Search... (this may take a few minutes)\n",
            "Fitting 3 folds for each of 9 candidates, totalling 27 fits\n",
            "\n",
            "✅ Best Parameters: {'logreg__C': 1, 'tfidf__max_features': 3000}\n",
            "🎯 Best Accuracy: 0.894903515366915\n"
          ]
        }
      ],
      "source": [
        "# Please execute all the cells above this one before running this cell.\n",
        "print(\"🚀 Starting Grid Search... (this may take a few minutes)\")\n",
        "from sklearn.pipeline import Pipeline\n",
        "from sklearn.feature_extraction.text import TfidfVectorizer\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "\n",
        "# Define the pipeline\n",
        "pipeline = Pipeline([\n",
        "    ('tfidf', TfidfVectorizer()),\n",
        "    ('logreg', LogisticRegression())\n",
        "])\n",
        "\n",
        "# Define the parameter grid for GridSearchCV\n",
        "param_grid = {\n",
        "    'tfidf__max_features': [1000, 2000, 3000],\n",
        "    'logreg__C': [0.1, 1, 10]\n",
        "}\n",
        "\n",
        "# Assuming X and y are defined earlier in the notebook\n",
        "# If not, you'll need to define them here based on your dataframe\n",
        "# Example:\n",
        "X = df['cleaned'] # or df['lemmatized_tokens'] depending on which pre-processed text you want to use\n",
        "y = df['sentiment']\n",
        "\n",
        "\n",
        "grid_search = GridSearchCV(pipeline, param_grid=param_grid, cv=3, scoring='accuracy', verbose=1)\n",
        "grid_search.fit(X, y)\n",
        "\n",
        "print(\"\\n✅ Best Parameters:\", grid_search.best_params_)\n",
        "print(\"🎯 Best Accuracy:\", grid_search.best_score_)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "f4G5UCf9CLxV"
      },
      "source": [
        "##Check Balanced"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 233
        },
        "id": "Ats1VlPbCQtI",
        "outputId": "d1f29b1b-c858-497c-a785-36d6aa21d96d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "📊 Sentiment Distribution:\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "sentiment\n",
              "negative    22036\n",
              "positive    21857\n",
              "Name: count, dtype: int64"
            ],
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>count</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sentiment</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>negative</th>\n",
              "      <td>22036</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>positive</th>\n",
              "      <td>21857</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "print(\"📊 Sentiment Distribution:\")\n",
        "display(df['sentiment'].value_counts())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 197
        },
        "id": "yO_a6l_GC0SH",
        "outputId": "5997a25c-7efd-491c-f584-0f7171fa3705"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "sentiment\n",
            "negative    22036\n",
            "positive    21857\n",
            "Name: count, dtype: int64\n",
            "sentiment\n",
            "negative    50.203905\n",
            "positive    49.796095\n",
            "Name: proportion, dtype: float64\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# check counts\n",
        "print(df['sentiment'].value_counts())\n",
        "\n",
        "# check percentages\n",
        "print(df['sentiment'].value_counts(normalize=True) * 100)\n",
        "\n",
        "# visualize\n",
        "df['sentiment'].value_counts().plot(kind='bar')\n",
        "plt.title('Class Distribution')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "p_S4KHv5DPKl",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c8efa439-9697-4d9c-b2dc-24dce91abbd2"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Class Counts:\n",
            " sentiment\n",
            "negative    22036\n",
            "positive    21857\n",
            "Name: count, dtype: int64\n",
            "\n",
            "Class Percentages:\n",
            " sentiment\n",
            "negative    50.203905\n",
            "positive    49.796095\n",
            "Name: proportion, dtype: float64\n",
            "\n",
            "Conclusion: ✅ The dataset is BALANCED\n"
          ]
        }
      ],
      "source": [
        "# Check class counts\n",
        "counts = df['sentiment'].value_counts()\n",
        "print(\"Class Counts:\\n\", counts)\n",
        "\n",
        "# Check class percentages\n",
        "percentages = df['sentiment'].value_counts(normalize=True) * 100\n",
        "print(\"\\nClass Percentages:\\n\", percentages)\n",
        "\n",
        "# Decide if balanced or not\n",
        "max_percent = percentages.max()\n",
        "if max_percent > 60:\n",
        "    print(\"\\nConclusion: ❗ The dataset is UNBALANCED\")\n",
        "else:\n",
        "    print(\"\\nConclusion: ✅ The dataset is BALANCED\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5yxugaDwHnQp"
      },
      "source": [
        "# Machine learning models"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "t3kI7oaUIQGY"
      },
      "source": [
        "## SVM"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "o7oi18TMF1fQ",
        "outputId": "5ae494e8-897f-46de-88cb-77bfbcf733c6"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Baseline SVM Accuracy: 0.8907620457910924\n",
            "✅ Best Params: {'C': 0.1, 'loss': 'squared_hinge'}\n",
            "🎯 Best CV Accuracy: 0.8965654048039171\n",
            "✅ Optimized SVM Accuracy: 0.8948627406310514\n",
            "\n",
            "Classification Report:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "    negative       0.88      0.91      0.90      4396\n",
            "    positive       0.91      0.88      0.89      4383\n",
            "\n",
            "    accuracy                           0.89      8779\n",
            "   macro avg       0.90      0.89      0.89      8779\n",
            "weighted avg       0.90      0.89      0.89      8779\n",
            "\n"
          ]
        }
      ],
      "source": [
        "from sklearn.feature_extraction.text import TfidfVectorizer\n",
        "from sklearn.svm import LinearSVC\n",
        "from sklearn.metrics import accuracy_score, classification_report # Added import for accuracy_score and classification_report\n",
        "\n",
        "# Columns\n",
        "TEXT_COL = 'cleaned'\n",
        "LABEL_COL = 'sentiment'\n",
        "\n",
        "# Features & Labels\n",
        "X = df[TEXT_COL]\n",
        "y = df[LABEL_COL]\n",
        "\n",
        "# Split data into training and testing sets\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
        "\n",
        "# Initialize TfidfVectorizer\n",
        "tfidf_vectorizer = TfidfVectorizer()\n",
        "\n",
        "# Fit and transform training data, transform testing data\n",
        "X_train_tfidf = tfidf_vectorizer.fit_transform(X_train)\n",
        "X_test_tfidf = tfidf_vectorizer.transform(X_test)\n",
        "\n",
        "\n",
        "# Train a baseline LinearSVC model\n",
        "svm = LinearSVC()\n",
        "svm.fit(X_train_tfidf, y_train)\n",
        "y_pred = svm.predict(X_test_tfidf)\n",
        "\n",
        "print(\"Baseline SVM Accuracy:\", accuracy_score(y_test, y_pred))\n",
        "\n",
        "# Define parameter grid for GridSearchCV\n",
        "param_grid = {\n",
        "    'C': [0.01, 0.1, 1, 10, 100],\n",
        "    'loss': ['hinge', 'squared_hinge']\n",
        "}\n",
        "\n",
        "# Create GridSearchCV object for LinearSVC\n",
        "grid_svm = GridSearchCV(\n",
        "    LinearSVC(),\n",
        "    param_grid,\n",
        "    scoring='accuracy',\n",
        "    cv=5,\n",
        "    n_jobs=-1\n",
        ")\n",
        "\n",
        "# Fit GridSearchCV on the training data\n",
        "grid_svm.fit(X_train_tfidf, y_train)\n",
        "\n",
        "print(\"✅ Best Params:\", grid_svm.best_params_)\n",
        "print(\"🎯 Best CV Accuracy:\", grid_svm.best_score_)\n",
        "\n",
        "# Train the best estimator on the training data\n",
        "best_svm = grid_svm.best_estimator_\n",
        "best_svm.fit(X_train_tfidf, y_train)\n",
        "\n",
        "# Make predictions on the test set with the best model\n",
        "y_pred_best = best_svm.predict(X_test_tfidf)\n",
        "\n",
        "print(\"✅ Optimized SVM Accuracy:\", accuracy_score(y_test, y_pred_best))\n",
        "print(\"\\nClassification Report:\")\n",
        "print(classification_report(y_test, y_pred_best))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gm9T8n3BOM-o"
      },
      "source": [
        "## Logistic Regression"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iBmRxettMXVw",
        "outputId": "cfa9d393-83ca-4c86-cff6-ac0c455fc4a2"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "✅ Logistic Regression Accuracy: 0.8928123932110719\n",
            "\n",
            "Classification Report:\n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "    negative       0.88      0.91      0.89      4396\n",
            "    positive       0.91      0.87      0.89      4383\n",
            "\n",
            "    accuracy                           0.89      8779\n",
            "   macro avg       0.89      0.89      0.89      8779\n",
            "weighted avg       0.89      0.89      0.89      8779\n",
            "\n"
          ]
        }
      ],
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.feature_extraction.text import TfidfVectorizer\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.metrics import accuracy_score, classification_report\n",
        "\n",
        "# Columns\n",
        "TEXT_COL = 'cleaned'\n",
        "LABEL_COL = 'sentiment'\n",
        "\n",
        "# Features & Labels\n",
        "X = df[TEXT_COL]\n",
        "y = df[LABEL_COL]\n",
        "\n",
        "# Train/Test Split\n",
        "X_train, X_test, y_train, y_test = train_test_split(\n",
        "    X, y, test_size=0.2, random_state=42\n",
        ")\n",
        "\n",
        "# TF-IDF\n",
        "tfidf = TfidfVectorizer(\n",
        "    max_features=10000,\n",
        "    ngram_range=(1,2),\n",
        "    stop_words='english'\n",
        ")\n",
        "\n",
        "X_train_tfidf = tfidf.fit_transform(X_train)\n",
        "X_test_tfidf = tfidf.transform(X_test)\n",
        "\n",
        "# Logistic Regression (tuned manually for speed + accuracy)\n",
        "log_reg = LogisticRegression(\n",
        "    C=1.0,                 # good default value\n",
        "    penalty='l2',          # stable\n",
        "    solver='liblinear',    # good for small/medium data\n",
        "    max_iter=2000\n",
        ")\n",
        "\n",
        "# Train\n",
        "log_reg.fit(X_train_tfidf, y_train)\n",
        "\n",
        "# Predict\n",
        "y_pred = log_reg.predict(X_test_tfidf)\n",
        "\n",
        "# Evaluation\n",
        "print(\"✅ Logistic Regression Accuracy:\", accuracy_score(y_test, y_pred))\n",
        "print(\"\\nClassification Report:\\n\", classification_report(y_test, y_pred))\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "J-gJgwfDUzGt"
      },
      "source": [
        "## Random Forest"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rtH7WdWpU2b3",
        "outputId": "672e098a-869e-4fb1-dc07-68eca398e62b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "✅ Random Forest Accuracy: 0.8814215742111858\n",
            "\n",
            "Classification Report:\n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "    negative       0.87      0.90      0.88      4396\n",
            "    positive       0.90      0.86      0.88      4383\n",
            "\n",
            "    accuracy                           0.88      8779\n",
            "   macro avg       0.88      0.88      0.88      8779\n",
            "weighted avg       0.88      0.88      0.88      8779\n",
            "\n"
          ]
        }
      ],
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.feature_extraction.text import TfidfVectorizer\n",
        "from sklearn.metrics import accuracy_score, classification_report\n",
        "from sklearn.ensemble import RandomForestClassifier # Added import\n",
        "\n",
        "# Columns\n",
        "TEXT_COL = 'cleaned'\n",
        "LABEL_COL = 'sentiment'\n",
        "\n",
        "# Features & Labels\n",
        "X = df[TEXT_COL]\n",
        "y = df[LABEL_COL]\n",
        "\n",
        "# Train/Test Split\n",
        "X_train, X_test, y_train, y_test = train_test_split(\n",
        "    X, y, test_size=0.2, random_state=42\n",
        ")\n",
        "\n",
        "# TF-IDF\n",
        "tfidf = TfidfVectorizer(\n",
        "    max_features=15000,\n",
        "    ngram_range=(1,2),\n",
        "    stop_words='english'\n",
        ")\n",
        "\n",
        "X_train_tfidf = tfidf.fit_transform(X_train)\n",
        "X_test_tfidf = tfidf.transform(X_test)\n",
        "\n",
        "# Random Forest Model (fast baseline)\n",
        "rf = RandomForestClassifier(\n",
        "    n_estimators=200,        # number of trees\n",
        "    max_depth=None,         # allow full depth\n",
        "    min_samples_split=2,\n",
        "    min_samples_leaf=1,\n",
        "    random_state=42,\n",
        "    n_jobs=-1               # use all CPU cores (faster)\n",
        ")\n",
        "\n",
        "# Train\n",
        "rf.fit(X_train_tfidf, y_train)\n",
        "\n",
        "# Predict\n",
        "y_pred = rf.predict(X_test_tfidf)\n",
        "\n",
        "# Evaluation\n",
        "print(\"✅ Random Forest Accuracy:\", accuracy_score(y_test, y_pred))\n",
        "print(\"\\nClassification Report:\\n\", classification_report(y_test, y_pred))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uDOLWIOsWhjt"
      },
      "source": [
        "## LightGBM"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jVYF-CBSSTkm",
        "outputId": "b6e3a3d3-a2ed-48cc-e985-b34356881874"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[LightGBM] [Info] Number of positive: 17474, number of negative: 17640\n",
            "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.111211 seconds.\n",
            "You can set `force_row_wise=true` to remove the overhead.\n",
            "And if memory is not enough, you can set `force_col_wise=true`.\n",
            "[LightGBM] [Info] Total Bins 86642\n",
            "[LightGBM] [Info] Number of data points in the train set: 35114, number of used features: 2660\n",
            "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.497636 -> initscore=-0.009455\n",
            "[LightGBM] [Info] Start training from score -0.009455\n",
            "✅ LightGBM Accuracy: 0.8892812393211071\n",
            "\n",
            "Classification Report:\n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "    negative       0.89      0.89      0.89      4396\n",
            "    positive       0.89      0.89      0.89      4383\n",
            "\n",
            "    accuracy                           0.89      8779\n",
            "   macro avg       0.89      0.89      0.89      8779\n",
            "weighted avg       0.89      0.89      0.89      8779\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.12/dist-packages/sklearn/utils/validation.py:2739: UserWarning: X does not have valid feature names, but LGBMClassifier was fitted with feature names\n",
            "  warnings.warn(\n"
          ]
        }
      ],
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.feature_extraction.text import TfidfVectorizer\n",
        "from sklearn.metrics import accuracy_score, classification_report\n",
        "import lightgbm as lgb   # <-- REQUIRED IMPORT\n",
        "\n",
        "# Columns\n",
        "TEXT_COL = 'cleaned'\n",
        "LABEL_COL = 'sentiment'\n",
        "\n",
        "# Features & Labels\n",
        "X = df[TEXT_COL]\n",
        "y = df[LABEL_COL]\n",
        "\n",
        "# Train/Test Split\n",
        "X_train, X_test, y_train, y_test = train_test_split(\n",
        "    X, y, test_size=0.2, random_state=42\n",
        ")\n",
        "\n",
        "# TF-IDF\n",
        "tfidf = TfidfVectorizer(\n",
        "    max_features=15000,\n",
        "    ngram_range=(1,2),\n",
        "    stop_words='english'\n",
        ")\n",
        "\n",
        "X_train_tfidf = tfidf.fit_transform(X_train)\n",
        "X_test_tfidf = tfidf.transform(X_test)\n",
        "\n",
        "# =============================\n",
        "#   LightGBM Model\n",
        "# =============================\n",
        "lgb_model = lgb.LGBMClassifier(\n",
        "    n_estimators=500,\n",
        "    learning_rate=0.05,\n",
        "    max_depth=-1,\n",
        "    num_leaves=64,\n",
        "    subsample=0.8,\n",
        "    colsample_bytree=0.8,\n",
        "    objective='multiclass' if y.nunique() > 2 else 'binary',\n",
        "    random_state=42,\n",
        "    n_jobs=-1\n",
        ")\n",
        "\n",
        "# Train\n",
        "lgb_model.fit(X_train_tfidf, y_train)\n",
        "\n",
        "# Predict\n",
        "y_pred = lgb_model.predict(X_test_tfidf)\n",
        "\n",
        "# Accuracy\n",
        "print(\"✅ LightGBM Accuracy:\", accuracy_score(y_test, y_pred))\n",
        "print(\"\\nClassification Report:\\n\", classification_report(y_test, y_pred))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DuEcXTWjYO23"
      },
      "source": [
        "## Naive Bayes"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hb1EsJ8DVMLf",
        "outputId": "bf29bba0-a86f-4d9c-a199-a99f8faebd5e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "✅ Naive Bayes Accuracy: 0.8834719216311653\n",
            "\n",
            "Classification Report:\n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "    negative       0.85      0.93      0.89      4396\n",
            "    positive       0.92      0.84      0.88      4383\n",
            "\n",
            "    accuracy                           0.88      8779\n",
            "   macro avg       0.89      0.88      0.88      8779\n",
            "weighted avg       0.89      0.88      0.88      8779\n",
            "\n"
          ]
        }
      ],
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.feature_extraction.text import TfidfVectorizer\n",
        "from sklearn.metrics import accuracy_score, classification_report\n",
        "from sklearn.naive_bayes import MultinomialNB\n",
        "from scipy.sparse import hstack\n",
        "\n",
        "# 1) Columns\n",
        "TEXT_COL = 'cleaned'\n",
        "LABEL_COL = 'sentiment'\n",
        "\n",
        "# 2) Train / Test Split\n",
        "X = df[TEXT_COL]\n",
        "y = df[LABEL_COL]\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(\n",
        "    X, y, test_size=0.2, random_state=42\n",
        ")\n",
        "\n",
        "# 3) TF-IDF Word-level\n",
        "tfidf_word = TfidfVectorizer(\n",
        "    max_features=20000,\n",
        "    ngram_range=(1,2),\n",
        "    stop_words='english'\n",
        ")\n",
        "X_train_word = tfidf_word.fit_transform(X_train)\n",
        "X_test_word = tfidf_word.transform(X_test)\n",
        "\n",
        "# 4) TF-IDF Char-level\n",
        "tfidf_char = TfidfVectorizer(\n",
        "    analyzer='char',\n",
        "    ngram_range=(3,5),\n",
        "    max_features=20000\n",
        ")\n",
        "X_train_char = tfidf_char.fit_transform(X_train)\n",
        "X_test_char = tfidf_char.transform(X_test)\n",
        "\n",
        "# 5) Merge Word + Char TF-IDF\n",
        "X_train_combined = hstack([X_train_word, X_train_char])\n",
        "X_test_combined = hstack([X_test_word, X_test_char])\n",
        "\n",
        "# 6) Naive Bayes Model\n",
        "nb_model = MultinomialNB()\n",
        "nb_model.fit(X_train_combined, y_train)\n",
        "\n",
        "# 7) Predictions\n",
        "y_pred = nb_model.predict(X_test_combined)\n",
        "\n",
        "# 8) Evaluation\n",
        "print(\"✅ Naive Bayes Accuracy:\", accuracy_score(y_test, y_pred))\n",
        "print(\"\\nClassification Report:\\n\", classification_report(y_test, y_pred))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4Gi7j93o22Kg"
      },
      "source": [
        "# LLM"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LJiJ7yu7HWN5"
      },
      "source": [
        "## 1-Distilbert-Base-Uncased"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# ============================================================\n",
        "# 0) INSTALL DEPENDENCIES\n",
        "# ============================================================\n",
        "!pip install -q transformers datasets accelerate evaluate scipy nltk\n",
        "\n",
        "# ============================================================\n",
        "# 1) IMPORTS\n",
        "# ============================================================\n",
        "import re\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "from scipy.signal import savgol_filter\n",
        "\n",
        "import nltk\n",
        "from nltk.corpus import stopwords\n",
        "\n",
        "from transformers import (\n",
        "    AutoTokenizer,\n",
        "    AutoModelForSequenceClassification,\n",
        "    Trainer,\n",
        "    TrainingArguments,\n",
        "    EarlyStoppingCallback,\n",
        "    TrainerCallback\n",
        ")\n",
        "from datasets import Dataset\n",
        "import evaluate\n",
        "\n",
        "# ============================================================\n",
        "# 2) LOAD + CLEAN DATA\n",
        "# ============================================================\n",
        "nltk.download(\"stopwords\")\n",
        "stop_words = set(stopwords.words(\"english\"))\n",
        "\n",
        "df = pd.read_csv(path + '/mobile_legends_reviews.csv')\n",
        "\n",
        "df[\"content\"] = df[\"content\"].astype(str).str.lower()\n",
        "\n",
        "def remove_emoji(text):\n",
        "    return re.sub(\n",
        "        \"[\"u\"\\U0001F600-\\U0001F64F\"\n",
        "          u\"\\U0001F300-\\U0001F5FF\"\n",
        "          u\"\\U0001F680-\\U0001F6FF\"\n",
        "          u\"\\U0001F1E0-\\U0001F1FF\"\n",
        "        \"]+\", \"\", text\n",
        "    )\n",
        "\n",
        "df[\"content\"] = df[\"content\"].apply(remove_emoji)\n",
        "df[\"content\"] = df[\"content\"].str.replace(r\"[^a-zA-Z0-9\\s.,!?;:]\", \"\", regex=True)\n",
        "\n",
        "def remove_stopwords(text):\n",
        "    return \" \".join([w for w in text.split() if w not in stop_words])\n",
        "\n",
        "df[\"cleaned\"] = df[\"content\"].apply(remove_stopwords)\n",
        "\n",
        "df = df[df[\"score\"] != 3].reset_index(drop=True)\n",
        "\n",
        "def label_sentiment(s):\n",
        "    return \"negative\" if s <= 2 else \"positive\"\n",
        "\n",
        "df[\"sentiment\"] = df[\"score\"].apply(label_sentiment)\n",
        "\n",
        "df = df.drop_duplicates(subset=[\"cleaned\"])\n",
        "df = df[[\"cleaned\", \"sentiment\"]]\n",
        "\n",
        "# ============================================================\n",
        "# 3) DATASET + TOKENIZATION\n",
        "# ============================================================\n",
        "dataset = Dataset.from_pandas(df)\n",
        "dataset = dataset.class_encode_column(\"sentiment\")\n",
        "dataset = dataset.train_test_split(test_size=0.2, seed=42)\n",
        "\n",
        "model_name = \"distilbert-base-uncased\"\n",
        "tokenizer = AutoTokenizer.from_pretrained(model_name)\n",
        "\n",
        "def tokenize(batch):\n",
        "    return tokenizer(\n",
        "        batch[\"cleaned\"],\n",
        "        padding=\"max_length\",\n",
        "        truncation=True,\n",
        "        max_length=128,\n",
        "    )\n",
        "\n",
        "tokenized = dataset.map(tokenize, batched=True)\n",
        "tokenized = tokenized.remove_columns([\"cleaned\"]).rename_column(\"sentiment\", \"labels\")\n",
        "tokenized.set_format(\"torch\")\n",
        "\n",
        "# ============================================================\n",
        "# 4) MODEL\n",
        "# ============================================================\n",
        "model = AutoModelForSequenceClassification.from_pretrained(\n",
        "    model_name,\n",
        "    num_labels=2\n",
        ")\n",
        "\n",
        "model.config.dropout = 0.2\n",
        "model.config.attention_dropout = 0.2\n",
        "model.config.label_smoothing = 0.1\n",
        "\n",
        "# ============================================================\n",
        "# 5) TRAINING ARGS\n",
        "# ============================================================\n",
        "accuracy_metric = evaluate.load(\"accuracy\")\n",
        "\n",
        "def compute_metrics(pred):\n",
        "    logits, labels = pred\n",
        "    preds = np.argmax(logits, axis=-1)\n",
        "    return accuracy_metric.compute(predictions=preds, references=labels)\n",
        "\n",
        "training_args = TrainingArguments(\n",
        "    output_dir=\"distilbert_smooth\",\n",
        "    eval_strategy=\"steps\",\n",
        "    save_strategy=\"steps\",\n",
        "    eval_steps=100,\n",
        "    save_steps=300,\n",
        "    learning_rate=1e-5,\n",
        "    warmup_ratio=0.1,\n",
        "    lr_scheduler_type=\"cosine\",\n",
        "    num_train_epochs=3,\n",
        "    per_device_train_batch_size=16,\n",
        "    per_device_eval_batch_size=16,\n",
        "    gradient_accumulation_steps=2,\n",
        "    weight_decay=0.15,\n",
        "    logging_steps=50,\n",
        "    load_best_model_at_end=True,\n",
        "    metric_for_best_model=\"eval_accuracy\",\n",
        "    greater_is_better=True,\n",
        "    fp16=True,\n",
        "    report_to=\"none\",\n",
        ")\n",
        "\n",
        "# ============================================================\n",
        "# 6–9) METRICS CALLBACK + TRAINER + TRAIN + PLOTS\n",
        "# ============================================================\n",
        "\n",
        "from transformers import TrainerCallback\n",
        "from scipy.signal import savgol_filter\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# -------------------- CALLBACK (LOG TRAIN + VAL METRICS) --------------------\n",
        "class MetricsCallback(TrainerCallback):\n",
        "    def __init__(self):\n",
        "        self.train_steps = []\n",
        "        self.train_loss = []\n",
        "        self.train_acc = []\n",
        "        self.eval_steps = []\n",
        "        self.val_loss = []\n",
        "        self.val_acc = []\n",
        "\n",
        "    def on_log(self, args, state, control, logs=None, **kwargs):\n",
        "        if logs is None:\n",
        "            return\n",
        "\n",
        "        step = state.global_step\n",
        "\n",
        "        # Training loss\n",
        "        if \"loss\" in logs and \"eval_loss\" not in logs:\n",
        "            self.train_steps.append(step)\n",
        "            self.train_loss.append(logs[\"loss\"])\n",
        "\n",
        "        # Training accuracy\n",
        "        if \"accuracy\" in logs and \"eval_accuracy\" not in logs:\n",
        "            self.train_acc.append(logs[\"accuracy\"])\n",
        "\n",
        "        # Validation loss\n",
        "        if \"eval_loss\" in logs:\n",
        "            self.eval_steps.append(step)\n",
        "            self.val_loss.append(logs[\"eval_loss\"])\n",
        "\n",
        "        # Validation accuracy\n",
        "        if \"eval_accuracy\" in logs:\n",
        "            self.val_acc.append(logs[\"eval_accuracy\"])\n",
        "\n",
        "\n",
        "# ---------------------------- TRAINER ----------------------------\n",
        "metrics_callback = MetricsCallback()\n",
        "\n",
        "trainer = Trainer(\n",
        "    model=model,\n",
        "    args=training_args,\n",
        "    train_dataset=tokenized[\"train\"],\n",
        "    eval_dataset=tokenized[\"test\"],\n",
        "    compute_metrics=compute_metrics,\n",
        "    callbacks=[EarlyStoppingCallback(early_stopping_patience=2), metrics_callback],\n",
        ")\n",
        "\n",
        "# ---------------------------- TRAIN ----------------------------\n",
        "trainer.train()\n",
        "\n",
        "print(\"\\nTEST RESULTS:\")\n",
        "print(trainer.evaluate(tokenized[\"test\"]))\n",
        "\n",
        "\n",
        "# ---------------------------- PLOTS ----------------------------\n",
        "\n",
        "# Load metrics\n",
        "train_steps = metrics_callback.train_steps\n",
        "train_loss  = metrics_callback.train_loss\n",
        "train_acc   = metrics_callback.train_acc\n",
        "eval_steps  = metrics_callback.eval_steps\n",
        "val_loss    = metrics_callback.val_loss\n",
        "val_acc     = metrics_callback.val_acc\n",
        "\n",
        "# Smooth training loss\n",
        "if len(train_loss) >= 7:\n",
        "    train_loss_smooth = savgol_filter(train_loss, 7, 3)\n",
        "else:\n",
        "    train_loss_smooth = train_loss\n",
        "\n",
        "# -------- ACCURACY GRAPH --------\n",
        "plt.figure(figsize=(7,5))\n",
        "plt.plot(train_steps[:len(train_acc)], train_acc, label=\"Train Accuracy\")\n",
        "plt.plot(eval_steps, val_acc, label=\"Validation Accuracy\")\n",
        "plt.title(\"Accuracy Curve\")\n",
        "plt.xlabel(\"Global Step\")\n",
        "plt.ylabel(\"Accuracy\")\n",
        "plt.legend()\n",
        "plt.grid(True)\n",
        "plt.show()\n",
        "\n",
        "# -------- LOSS GRAPH --------\n",
        "plt.figure(figsize=(7,5))\n",
        "plt.plot(train_steps, train_loss_smooth, label=\"Train Loss\")\n",
        "plt.plot(eval_steps, val_loss, label=\"Validation Loss\")\n",
        "plt.title(\"Loss Curve\")\n",
        "plt.xlabel(\"Global Step\")\n",
        "plt.ylabel(\"Loss\")\n",
        "plt.legend()\n",
        "plt.grid(True)\n",
        "plt.show()"
      ],
      "metadata": {
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        "outputId": "a05c15a9-395d-41c8-b55a-0df22943f8db"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[?25l   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/84.1 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m84.1/84.1 kB\u001b[0m \u001b[31m7.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Package stopwords is already up-to-date!\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Casting to class labels:   0%|          | 0/40910 [00:00<?, ? examples/s]"
            ],
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              "version_minor": 0,
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        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.12/dist-packages/huggingface_hub/utils/_auth.py:94: UserWarning: \n",
            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
            "You will be able to reuse this secret in all of your notebooks.\n",
            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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              "vocab.txt:   0%|          | 0.00/232k [00:00<?, ?B/s]"
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        {
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              "tokenizer.json:   0%|          | 0.00/466k [00:00<?, ?B/s]"
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          "data": {
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              "Map:   0%|          | 0/32728 [00:00<?, ? examples/s]"
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          "data": {
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              "Map:   0%|          | 0/8182 [00:00<?, ? examples/s]"
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "model.safetensors:   0%|          | 0.00/268M [00:00<?, ?B/s]"
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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of DistilBertForSequenceClassification were not initialized from the model checkpoint at distilbert-base-uncased and are newly initialized: ['classifier.bias', 'classifier.weight', 'pre_classifier.bias', 'pre_classifier.weight']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading builder script: 0.00B [00:00, ?B/s]"
            ],
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              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='1000' max='3069' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [1000/3069 01:46 < 03:39, 9.41 it/s, Epoch 0/3]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Step</th>\n",
              "      <th>Training Loss</th>\n",
              "      <th>Validation Loss</th>\n",
              "      <th>Accuracy</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>100</td>\n",
              "      <td>0.670300</td>\n",
              "      <td>0.637102</td>\n",
              "      <td>0.527377</td>\n",
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              "    <tr>\n",
              "      <td>200</td>\n",
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              "      <td>0.371528</td>\n",
              "      <td>0.865070</td>\n",
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              "    <tr>\n",
              "      <td>300</td>\n",
              "      <td>0.311800</td>\n",
              "      <td>0.296273</td>\n",
              "      <td>0.886458</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>400</td>\n",
              "      <td>0.293300</td>\n",
              "      <td>0.282714</td>\n",
              "      <td>0.894769</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>500</td>\n",
              "      <td>0.302800</td>\n",
              "      <td>0.269494</td>\n",
              "      <td>0.897336</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>600</td>\n",
              "      <td>0.287700</td>\n",
              "      <td>0.261586</td>\n",
              "      <td>0.901369</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>700</td>\n",
              "      <td>0.258700</td>\n",
              "      <td>0.271352</td>\n",
              "      <td>0.899413</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>800</td>\n",
              "      <td>0.289400</td>\n",
              "      <td>0.252840</td>\n",
              "      <td>0.905402</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>900</td>\n",
              "      <td>0.270400</td>\n",
              "      <td>0.254442</td>\n",
              "      <td>0.903691</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1000</td>\n",
              "      <td>0.254500</td>\n",
              "      <td>0.253914</td>\n",
              "      <td>0.904669</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "TEST RESULTS:\n"
          ]
        },
        {
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              "<IPython.core.display.HTML object>"
            ],
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              "    <div>\n",
              "      \n",
              "      <progress value='512' max='512' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [512/512 00:04]\n",
              "    </div>\n",
              "    "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'eval_loss': 0.2615855634212494, 'eval_accuracy': 0.9013688584698117, 'eval_runtime': 4.1707, 'eval_samples_per_second': 1961.796, 'eval_steps_per_second': 122.762, 'epoch': 0.9775171065493646}\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 700x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 700x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import classification_report, accuracy_score\n",
        "import numpy as np\n",
        "\n",
        "# Get true labels from the tokenized test dataset\n",
        "y_true_labels = tokenized[\"test\"][\"labels\"]\n",
        "\n",
        "# Get predictions from the trained model\n",
        "predictions = trainer.predict(tokenized[\"test\"])\n",
        "\n",
        "# Convert logits to predicted labels\n",
        "y_pred_labels = np.argmax(predictions.predictions, axis=-1)\n",
        "\n",
        "# Get the mapping from integer labels back to sentiment names\n",
        "label_names = tokenized[\"test\"].features[\"labels\"].names\n",
        "\n",
        "print(\"Accuracy:\", accuracy_score(y_true_labels, y_pred_labels))\n",
        "print(\"\\nClassification Report:\")\n",
        "print(classification_report(y_true_labels, y_pred_labels, digits=4, target_names=label_names))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 231
        },
        "id": "sq3fH5BAVkD9",
        "outputId": "54fdb5d2-d593-44d3-b25d-6e41b24f403b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": []
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 0.9013688584698117\n",
            "\n",
            "Classification Report:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "    negative     0.8901    0.9274    0.9084      4314\n",
            "    positive     0.9151    0.8723    0.8932      3868\n",
            "\n",
            "    accuracy                         0.9014      8182\n",
            "   macro avg     0.9026    0.8999    0.9008      8182\n",
            "weighted avg     0.9019    0.9014    0.9012      8182\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "g9S7ZQlwMZiM"
      },
      "source": [
        "## 2- DEBERTA-V3-BASE"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
          "referenced_widgets": [
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            "4448432200254f4e9761f92e52a6a9b5",
            "03506f5ffb9841bf8113b4452be15f43",
            "dd76ee0a588c42faa22986cc6f05ee51",
            "add0fbdfcd124852b7ab0db9c1ffa4ae",
            "be63f058a223480ebbcec1beda837b56",
            "7d369619a8fa47e5b9a40972917f1f39",
            "7f7267b525b94881a106027adbdc78a1",
            "5ac787c532c74b48a1ae3b9ae011505e",
            "6fbc3c98f8c54782b248882a928caba0",
            "0cb90eea68b24c2d98f0e30611e4bcf8",
            "77cde8ce399747879d22cef51575e80f"
          ]
        },
        "id": "Yf8gmEz4MSuT",
        "outputId": "18047744-db7a-4ea2-a508-0665bccb90e3"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Casting to class labels:   0%|          | 0/40910 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "8097a118fd574fde8bc83fbd801a45dc"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/52.0 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
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        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.12/dist-packages/transformers/convert_slow_tokenizer.py:566: UserWarning: The sentencepiece tokenizer that you are converting to a fast tokenizer uses the byte fallback option which is not implemented in the fast tokenizers. In practice this means that the fast version of the tokenizer can produce unknown tokens whereas the sentencepiece version would have converted these unknown tokens into a sequence of byte tokens matching the original piece of text.\n",
            "  warnings.warn(\n"
          ]
        },
        {
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          "data": {
            "text/plain": [
              "Map:   0%|          | 0/32728 [00:00<?, ? examples/s]"
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              "Map:   0%|          | 0/8182 [00:00<?, ? examples/s]"
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          "data": {
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              "pytorch_model.bin:   0%|          | 0.00/371M [00:00<?, ?B/s]"
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          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of DebertaV2ForSequenceClassification were not initialized from the model checkpoint at microsoft/deberta-v3-base and are newly initialized: ['classifier.bias', 'classifier.weight', 'pooler.dense.bias', 'pooler.dense.weight']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "model.safetensors:   0%|          | 0.00/371M [00:00<?, ?B/s]"
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              "      [ 4550/14322 27:17 < 58:39, 2.78 it/s, Epoch 2/7]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Step</th>\n",
              "      <th>Training Loss</th>\n",
              "      <th>Validation Loss</th>\n",
              "      <th>Accuracy</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>350</td>\n",
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              "      <td>2100</td>\n",
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              "    <tr>\n",
              "      <td>3150</td>\n",
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              "      <td>0.239607</td>\n",
              "      <td>0.916280</td>\n",
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              "    <tr>\n",
              "      <td>3500</td>\n",
              "      <td>0.258100</td>\n",
              "      <td>0.238546</td>\n",
              "      <td>0.915669</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3850</td>\n",
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              "      <td>0.258441</td>\n",
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              "    <tr>\n",
              "      <td>4200</td>\n",
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              "      \n",
              "      <progress value='512' max='512' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [512/512 00:33]\n",
              "    </div>\n",
              "    "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "🔥 FINAL ACCURACY: 0.9164018577364947 \n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x500 with 1 Axes>"
            ],
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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# ============================================================\n",
        "# 🔥 ULTIMATE HIGH-ACCURACY DEBERTA-V3 MODEL + PLOTS (FULL VERSION)\n",
        "# ============================================================\n",
        "\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "from scipy.signal import savgol_filter\n",
        "\n",
        "from datasets import Dataset\n",
        "from transformers import (\n",
        "    AutoTokenizer,\n",
        "    AutoModelForSequenceClassification,\n",
        "    TrainingArguments,\n",
        "    Trainer,\n",
        "    EarlyStoppingCallback,\n",
        "    TrainerCallback,\n",
        ")\n",
        "import evaluate\n",
        "\n",
        "\n",
        "# ============================================================\n",
        "# 1) DATASET\n",
        "# df must contain: cleaned, sentiment\n",
        "# ============================================================\n",
        "\n",
        "dataset = Dataset.from_pandas(df)\n",
        "dataset = dataset.class_encode_column(\"sentiment\")\n",
        "dataset = dataset.train_test_split(test_size=0.2, seed=42)\n",
        "\n",
        "\n",
        "# ============================================================\n",
        "# 2) TOKENIZER\n",
        "# ============================================================\n",
        "\n",
        "model_name = \"microsoft/deberta-v3-base\"\n",
        "tokenizer = AutoTokenizer.from_pretrained(model_name)\n",
        "\n",
        "def tokenize(batch):\n",
        "    return tokenizer(\n",
        "        batch[\"cleaned\"],\n",
        "        truncation=True,\n",
        "        padding=\"max_length\",\n",
        "        max_length=256,\n",
        "    )\n",
        "\n",
        "tokenized = dataset.map(tokenize, batched=True)\n",
        "tokenized = tokenized.remove_columns([\"cleaned\"])\n",
        "tokenized = tokenized.rename_column(\"sentiment\", \"labels\")\n",
        "tokenized.set_format(\"torch\")\n",
        "\n",
        "\n",
        "# ============================================================\n",
        "# 3) MODEL + STRONG ANTI-OVERFITTING CONFIG\n",
        "# ============================================================\n",
        "\n",
        "num_labels = len(tokenized[\"train\"].features[\"labels\"].names)\n",
        "\n",
        "model = AutoModelForSequenceClassification.from_pretrained(\n",
        "    model_name,\n",
        "    num_labels=num_labels\n",
        ")\n",
        "\n",
        "model.config.hidden_dropout_prob = 0.30\n",
        "model.config.attention_probs_dropout_prob = 0.30\n",
        "model.config.label_smoothing_factor = 0.15\n",
        "\n",
        "accuracy_metric = evaluate.load(\"accuracy\")\n",
        "\n",
        "def compute_metrics(eval_pred):\n",
        "    logits, labels = eval_pred\n",
        "    preds = np.argmax(logits, axis=-1)\n",
        "    return accuracy_metric.compute(predictions=preds, references=labels)\n",
        "\n",
        "\n",
        "# ============================================================\n",
        "# 4) METRICS CALLBACK (COLLECTS TRAIN/VAL METRICS FOR PLOTTING)\n",
        "# ============================================================\n",
        "\n",
        "class MetricsCallback(TrainerCallback):\n",
        "    def __init__(self):\n",
        "        self.train_steps = []\n",
        "        self.train_loss = []\n",
        "        self.train_acc = []\n",
        "        self.eval_steps = []\n",
        "        self.val_loss = []\n",
        "        self.val_acc = []\n",
        "\n",
        "    def on_log(self, args, state, control, logs=None, **kwargs):\n",
        "        if logs is None:\n",
        "            return\n",
        "\n",
        "        # Training loss\n",
        "        if \"loss\" in logs:\n",
        "            self.train_steps.append(state.global_step)\n",
        "            self.train_loss.append(logs[\"loss\"])\n",
        "\n",
        "        # Training accuracy (if logged)\n",
        "        if \"accuracy\" in logs:\n",
        "            self.train_acc.append(logs[\"accuracy\"])\n",
        "\n",
        "        # Validation metrics\n",
        "        if \"eval_loss\" in logs:\n",
        "            self.eval_steps.append(state.global_step)\n",
        "            self.val_loss.append(logs[\"eval_loss\"])\n",
        "\n",
        "        if \"eval_accuracy\" in logs:\n",
        "            self.val_acc.append(logs[\"eval_accuracy\"])\n",
        "\n",
        "\n",
        "metrics_callback = MetricsCallback()\n",
        "\n",
        "\n",
        "# ============================================================\n",
        "# 5) TRAINING ARGUMENTS\n",
        "# ============================================================\n",
        "\n",
        "training_args = TrainingArguments(\n",
        "    output_dir=\"deberta_v3_optimized\",\n",
        "    eval_strategy=\"steps\",\n",
        "    eval_steps=350,\n",
        "    save_strategy=\"steps\",\n",
        "    save_steps=350,\n",
        "    learning_rate=1e-5,\n",
        "    warmup_ratio=0.10,\n",
        "    weight_decay=0.18,\n",
        "    lr_scheduler_type=\"cosine\",\n",
        "    num_train_epochs=7,\n",
        "    per_device_train_batch_size=8,\n",
        "    per_device_eval_batch_size=16,\n",
        "    gradient_accumulation_steps=2,\n",
        "    logging_steps=80,\n",
        "    load_best_model_at_end=True,\n",
        "    metric_for_best_model=\"eval_accuracy\",\n",
        "    greater_is_better=True,\n",
        "    report_to=\"none\",\n",
        "    fp16=True,\n",
        ")\n",
        "\n",
        "\n",
        "# ============================================================\n",
        "# 6) TRAINER\n",
        "# ============================================================\n",
        "\n",
        "trainer = Trainer(\n",
        "    model=model,\n",
        "    args=training_args,\n",
        "    train_dataset=tokenized[\"train\"],\n",
        "    eval_dataset=tokenized[\"test\"],\n",
        "    compute_metrics=compute_metrics,\n",
        "    callbacks=[\n",
        "        metrics_callback,\n",
        "        EarlyStoppingCallback(early_stopping_patience=2)\n",
        "    ],\n",
        ")\n",
        "\n",
        "\n",
        "# ============================================================\n",
        "# 7) TRAIN\n",
        "# ============================================================\n",
        "\n",
        "train_output = trainer.train()\n",
        "\n",
        "\n",
        "# ============================================================\n",
        "# 8) FINAL EVALUATION\n",
        "# ============================================================\n",
        "\n",
        "results = trainer.evaluate()\n",
        "print(\"\\n🔥 FINAL ACCURACY:\", results[\"eval_accuracy\"], \"\\n\")\n",
        "\n",
        "\n",
        "# ============================================================\n",
        "# 9) PLOTS\n",
        "# ============================================================\n",
        "\n",
        "train_steps = metrics_callback.train_steps\n",
        "train_loss  = metrics_callback.train_loss\n",
        "train_acc   = metrics_callback.train_acc\n",
        "eval_steps  = metrics_callback.eval_steps\n",
        "val_loss    = metrics_callback.val_loss\n",
        "val_acc     = metrics_callback.val_acc\n",
        "\n",
        "# Smooth training loss using Savitzky-Golay\n",
        "if len(train_loss) >= 7:\n",
        "    train_loss_smooth = savgol_filter(train_loss, 7, 3)\n",
        "else:\n",
        "    train_loss_smooth = train_loss\n",
        "\n",
        "\n",
        "# -------- ACCURACY CURVE --------\n",
        "plt.figure(figsize=(8,5))\n",
        "plt.plot(train_steps[:len(train_acc)], train_acc, label=\"Train Accuracy\", color=\"blue\")\n",
        "plt.plot(eval_steps, val_acc, label=\"Validation Accuracy\", color=\"orange\")\n",
        "plt.title(\"Accuracy Curve (DeBERTa-v3)\")\n",
        "plt.xlabel(\"Global Step\")\n",
        "plt.ylabel(\"Accuracy\")\n",
        "plt.grid(True)\n",
        "plt.legend()\n",
        "plt.show()\n",
        "\n",
        "\n",
        "# -------- LOSS CURVE --------\n",
        "plt.figure(figsize=(8,5))\n",
        "plt.plot(train_steps, train_loss_smooth, label=\"Train Loss\", color=\"blue\")\n",
        "plt.plot(eval_steps, val_loss, label=\"Validation Loss\", color=\"orange\")\n",
        "plt.title(\"Loss Curve (DeBERTa-v3)\")\n",
        "plt.xlabel(\"Global Step\")\n",
        "plt.ylabel(\"Loss\")\n",
        "plt.grid(True)\n",
        "plt.legend()\n",
        "plt.show()\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import classification_report, accuracy_score\n",
        "import numpy as np\n",
        "\n",
        "# Get true labels from the tokenized test dataset (from the DeBERTa-v3-base section)\n",
        "y_true_labels = tokenized[\"test\"][\"labels\"]\n",
        "\n",
        "# Get predictions from the trained model (from the DeBERTa-v3-base section)\n",
        "predictions = trainer.predict(tokenized[\"test\"])\n",
        "\n",
        "# Convert logits to predicted labels\n",
        "y_pred_labels = np.argmax(predictions.predictions, axis=-1)\n",
        "\n",
        "# Get the mapping from integer labels back to sentiment names\n",
        "label_names = tokenized[\"test\"].features[\"labels\"].names\n",
        "\n",
        "print(\"Accuracy:\", accuracy_score(y_true_labels, y_pred_labels))\n",
        "print(\"\\nClassification Report:\")\n",
        "print(classification_report(y_true_labels, y_pred_labels, digits=4, target_names=label_names))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 231
        },
        "id": "_EPi7KMHeduF",
        "outputId": "e7fca567-15d8-4ab1-d1cd-2f82953a4014"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": []
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 0.9164018577364947\n",
            "\n",
            "Classification Report:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "    negative     0.8963    0.9516    0.9231      4314\n",
            "    positive     0.9420    0.8772    0.9084      3868\n",
            "\n",
            "    accuracy                         0.9164      8182\n",
            "   macro avg     0.9191    0.9144    0.9158      8182\n",
            "weighted avg     0.9179    0.9164    0.9162      8182\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uHaAMD7gF-s9"
      },
      "source": [
        "## 3-Roberta-Base"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
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            "89f5b9ef4246402ba9127d26dd3723b9",
            "60a85aeb96a9483da91b89f8f532c19c",
            "f184e86fa2e94d79a65f7c4e6a0b9cbe",
            "aa765828c5764851a6b00b80ccf65af7",
            "3efb342c381c4b5b9a9213f1f6e2a576",
            "987a7c51b3094e0bad68b4c54c4f5a87",
            "a1096b33aa0a43bf8fe74a66223d6bea",
            "fb219300ece84ed5b2dcb333946f0af7",
            "f534b45047a14a818df974589a27c654",
            "1f0ae811f679406b80e850b50087db4c",
            "df636ee3bc6d46e19ef2fed9dd5d5253",
            "f434c67c9c2842819193b0d89ed4ebcc",
            "0277a1c5e2e740d8a560af014e9c5e29",
            "9c50d2b5909341d9a2231feb8b200aa9",
            "c44c0fe2241d4fdc8d9b16b0a622a713",
            "331fe83de1eb420b9845374535f23e01",
            "e26fb6e075834dfe8189e43c00eb4daf",
            "48ffcc90e89c43e396f8545a7d48533c",
            "a18eedd275744719b5658cb039cf1ed5",
            "44104abcad8d4417a95b546ce165b979",
            "405c704ebd904270b729d137b6ed0263",
            "147259edd7b4433c912260d0a48fbff3",
            "09f3af94ae6b47dc9eeed3c5c0d18549",
            "4dad9ad9b36144f49ecfcb2fc58ab705",
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            "ab28bbc5ae5a4036b08d3f23eec84cb8",
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            "52b142142ab54d50859f89068dfc2527",
            "97d94a3629e34256ab17516a2222fc7b",
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            "a53ea33b35d24199a5add4daa8fc47d6",
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            "aeb5ea38d6174118909860f8fc6bdd57",
            "d167e6a8ee6e40f18850c814d9080dd8",
            "5d4e5c5cccf5475e8d09ae39bbe6bc4d",
            "e4d2038313fb48a4892a82881c5544ce",
            "ea9105c4dffd46c581ef31ecfdccd1f0",
            "a657595a3d244b53a736f86a8fc83d69",
            "fd83afd9c1c04de6b0771df66ed26960",
            "6e4aa42506ec4cadb0d6956ade3d255f",
            "c93b15ccc4644f2499ffb98d1bd73cfe",
            "46c48eb501f549c79fd7fbfa64b0575b",
            "f6220d9729e849e59734610b1b522f10",
            "a588ff8556834722903a21ec8cb896f5",
            "ba40bed93b4943bdad340a7e2640da5d",
            "33f63f9032334987a930d3a5c69b93da",
            "2769977375004be394c138f6868306eb",
            "68435b0f570f4cb8ae97e66db7f85db1",
            "8c2bbc9790d54c8d803e742c5ba9981d",
            "eaa09e9f7b9743a7936c2cc04c4cb539",
            "2ccaf8e4a1944bbea60e036dd555ff07",
            "97eb245828524bccb80d955c0d381c5b",
            "cee5eab8347048bc8493cdeec7c853bd",
            "ab22dcab78d1476fa7179f0fcda65a40",
            "c5038317c3d6478c9b54a4d07a891683",
            "1d3b62ff605641c48a6d3b3144e80fb3",
            "46b5c02935294ce79097188ad0e90a09",
            "851b44aed80a489ea3217d6ef462d85b",
            "f23e1c2a3cc34b4c8fa1ce8bb9cccdee",
            "e7fc49d6acbf4e22a8ea22f1193be32d",
            "bd89bce135814571833e2e5d0de44a04",
            "7062a8d2a75b43efae44c370c5580a30",
            "c48ed7811449453599a2aeb41694f6d2",
            "01c494d8fc624e179e5423e7c5680df6",
            "4a0b065cdf1943bb9bd0147abc86ae08",
            "c7e4386ecaa74e67b57273a9aeb6139b",
            "a9d1da514bd74ea994d5251d28f7a46a",
            "4f454eb991cf493a9df61c1bc84ad2b9",
            "8af5be8fbb7d45978afcc265ebd12611",
            "fa93a45721d54c35b37caa17c230ea4f",
            "777543100a8f4c2194dfa8bde2d6df33",
            "e4d3d5147b4c438785792ffa277aff67",
            "67987e992abb41e6b43f28e1a60114e6",
            "97efc1086c2c4640b09cfd173c9aab96",
            "b567f1e0ab534a7b92dd3c63d2e3c512",
            "7ce7d74159ab4e8185a561b3a1b8f3cd",
            "077cd8b34ee540a8b47f7d7722e0f14a",
            "6a4776e680c24c3e97af1b0658266f44",
            "0aa799950da54aa5bedc9d62a8da70e8",
            "fe7417abfe644228997fcc21939cc766",
            "f0d7d44db37445958184f9219e89a33b",
            "4d2b2f78424e461881e370633e065fcc",
            "f052485d08aa4e26aa4b8ef266afd408",
            "dcfb0920d4e54dbf9252898bcd21c224",
            "33977640fb774e1f8c419ff296579aef"
          ]
        },
        "id": "6WV2CWfPGAOC",
        "outputId": "8715acfa-95cf-4613-970a-1311bcfe75ad"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Package stopwords is already up-to-date!\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Casting to class labels:   0%|          | 0/45473 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "1fc47d86801c4be385df2e6ba702c913"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/25.0 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "893cce2448954c74953c67c4d93a05b8"
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "config.json:   0%|          | 0.00/481 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
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              "version_minor": 0,
              "model_id": "1f0ae811f679406b80e850b50087db4c"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "vocab.json:   0%|          | 0.00/899k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "405c704ebd904270b729d137b6ed0263"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "merges.txt:   0%|          | 0.00/456k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "a53ea33b35d24199a5add4daa8fc47d6"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer.json:   0%|          | 0.00/1.36M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "46c48eb501f549c79fd7fbfa64b0575b"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/36378 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "cee5eab8347048bc8493cdeec7c853bd"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/9095 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "01c494d8fc624e179e5423e7c5680df6"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "model.safetensors:   0%|          | 0.00/499M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "b567f1e0ab534a7b92dd3c63d2e3c512"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of RobertaForSequenceClassification were not initialized from the model checkpoint at roberta-base and are newly initialized: ['classifier.dense.bias', 'classifier.dense.weight', 'classifier.out_proj.bias', 'classifier.out_proj.weight']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='2100' max='9096' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [2100/9096 09:34 < 31:56, 3.65 it/s, Epoch 1/8]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Step</th>\n",
              "      <th>Training Loss</th>\n",
              "      <th>Validation Loss</th>\n",
              "      <th>Accuracy</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>300</td>\n",
              "      <td>0.411300</td>\n",
              "      <td>0.332081</td>\n",
              "      <td>0.869379</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>600</td>\n",
              "      <td>0.337200</td>\n",
              "      <td>0.285294</td>\n",
              "      <td>0.894338</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>900</td>\n",
              "      <td>0.322200</td>\n",
              "      <td>0.275140</td>\n",
              "      <td>0.896427</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1200</td>\n",
              "      <td>0.291200</td>\n",
              "      <td>0.278380</td>\n",
              "      <td>0.899615</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1500</td>\n",
              "      <td>0.292500</td>\n",
              "      <td>0.264307</td>\n",
              "      <td>0.903793</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1800</td>\n",
              "      <td>0.290400</td>\n",
              "      <td>0.270917</td>\n",
              "      <td>0.899615</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2100</td>\n",
              "      <td>0.284400</td>\n",
              "      <td>0.292196</td>\n",
              "      <td>0.901814</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='569' max='569' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [569/569 00:14]\n",
              "    </div>\n",
              "    "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "🔥 FINAL ACCURACY: 0.9037932930181418\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x500 with 1 Axes>"
            ],
            "image/png": 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LF0y2l/V3Z2lZCnvfd5fljTfegKurKzp16oQmTZpg/Pjx2LFjh8k5ap6Bonh4eODjjz9GXFwc4uLisGTJEoSEhODzzz/He++9Z3Kso6Oj2Yg+Hh4eqF+/vlkfGw8PD5P4WRqH0v7uilJYWb28vEzKdPr0aSQnJ8PX19f4gdmwpKWlGfslGe7ZpEkTk+v5+PiUqhlQUeU0fJBftmwZmjVrhoSEBJMP4Ib73x0/e3t7BAcHm8XExsYGERERiIiIQO/evfHMM89gy5YtSE5OxuTJk83KEBYWZjy+4HL3v5OdO3fG5s2bsWHDBsyZMweenp64detWqQZP+O2339ClSxc4OjqiVq1axmZthf2tiztNsSydI4eoumFiQVQDFPwP2yApKQk9evTAoUOHMGPGDPzvf//D5s2b8dFHHwGARcPLGj6g301Y0HZ9xIgR2Lp1K65fv46srCz897//xcMPP2zS0XL48OE4d+4c5s+fD39/f8yePRstWrTAH3/8UeL127dvj9DQUPzwww8AgB9++AFCCJPRoGbOnInIyEh0794d3333HTZu3IjNmzejRYsWFTq87sSJE/HBBx9g+PDh+PHHH7Fp0yZs3rwZ3t7elTasr5rfXXlp1qwZYmNjsWrVKtxzzz345ZdfcM8992Dq1KnGY8r6DHh7eyM3N7fY+UwA2efiySefxI4dO+Dp6Wk23GlRcaoK8btbUWUqSK/Xw9fXt9Bv6zdv3owZM2ZUSjkNH+THjBmDqKgoXL16Fc8++2y53qd+/foICQnB33//XeZr1K5dGxEREejTpw9eeeUVfPfdd1i3bl2h/ZYKs337duPIZAsXLsT69euxefNmPP7444U+K4YksHbt2mUuM1FVxuFmiWqobdu24caNG1izZg26d+9u3F6w82lFGjFiBKZPn45ffvkFfn5+SElJwaOPPmp2XN26dfHCCy/ghRdeQEJCAtq1a4cPPvgA/fr1K/EeI0eOxLvvvovDhw9j5cqVaNKkicnY9T///DPuu+8+LFmyxOS8pKSkUv/HHhgYCL1ej7Nnz5p8yxobG2t27M8//4zRo0fj//7v/4zbMjMzzUa7Kc23loGBgTh8+DD0er3JN9+GJm2GTsuVITAwsND3XVhZXFxcMGLECIwYMQLZ2dkYOnQoPvjgA0yePNk4TGxZnoHQ0FAA8nlu1apViWX28vJCo0aNcPTo0VK91+JYGgdLf3fl8S12o0aNsGXLFnTr1q3QLxwKlh2QNRzBwcHG7YmJieVSq1VQ3bp18fLLL2P69OnYtWsXunTpYrx/bGysyf2zs7Nx/vx5s9G+ipKbm4u0tLRyK+uAAQPQo0cPzJw5E88++yxcXFyKPf6XX36Bo6MjNm7cCAcHB+P2ZcuWFXq84d9ftXMPEVVVrLEgqqEM324W/NYsOzsbCxcurJT7N2vWDGFhYVi9ejVWr16NunXrmiQ4eXl5Zk0FfH194e/vX2g7+MIYaiemTJmCmJgYs7krbGxszL41/Omnn/Dvv/+W+v0YPuR+9tlnJtvnzZtndmxh950/f77ZsKCGDy2WDK/Zv39/XL161aR5WW5uLubPnw9XV1f06NHDkrdRLvr37489e/YgOjrauC09PR2LFi1CUFAQmjdvDgBmM5/b29ujefPmEEIgJydH1TNgmOhs3759JtsPHTpUaPv1Cxcu4Pjx44U2XSorS+Ng6e/OMPeMpcOtFmb48OHIy8sza/JluKfh2hEREbCzs8P8+fNNntXCnufyMHHiRDg7O+PDDz803t/e3h6fffaZyf2XLFmC5OTkQkdbu9upU6cQGxuL1q1bl2tZ33jjDdy4cQOLFy8u8VgbGxsoimLytx0XF4d169YVevz+/fuhKIrZRH1ENQVrLIhqqK5du8LLywujR4/GpEmToCgKVqxYUalNOUaMGIEpU6bA0dERTz31lMm3tampqahfvz6GDRuG1q1bw9XVFVu2bMHevXtNvukvTsOGDdG1a1f8+uuvAGCWWDz44IOYMWMGxo4di65du+LIkSP4/vvvTb4htVSbNm3w2GOPYeHChUhOTkbXrl0RFRWFM2fOmB374IMPYsWKFfDw8EDz5s0RHR2NLVu2mM0E3qZNG9jY2OCjjz5CcnIyHBwccP/998PX19fsms888wy++uorjBkzBvv370dQUBB+/vln7NixA/PmzYObm1up31Nxfvnll0I7+I8ePRpvvvkmfvjhB/Tr1w+TJk1CrVq18M033+D8+fP45ZdfjL/n3r17o06dOujWrRv8/Pxw4sQJfP755xgwYADc3NyQlJRU5mcgODgYLVu2xJYtW0wmG9u8eTOmTp2KQYMGoUuXLnB1dcW5c+ewdOlSZGVlmc0LoYalcbD0d+fk5ITmzZtj9erVaNq0KWrVqoWWLVuiZcuWFpepR48eePbZZzFr1izExMSgd+/esLOzw+nTp/HTTz/h008/xbBhw4xzYMyaNQsPPvgg+vfvj4MHD+KPP/4otDbPMM9NwcESSsPb2xtjx47FwoULceLECTRr1gyTJ0/G9OnT0bdvXwwaNAixsbFYuHAhOnbsaDYBXW5uLr777jsAsrlXXFwcvvzyS+j1epOmdQbbt29HZmam2fZWrVqVWMPVr18/tGzZEnPnzsX48eOLnUB0wIABmDt3Lvr27YvHH38cCQkJWLBgARo3bozDhw+bHb9582Z069bN7N8CohpDk7GoiKhMihputqghPXfs2CG6dOkinJychL+/v3j99dfFxo0bzYY4LWq42dmzZ5tdE0UMh1mY06dPG4d5/Oeff0z2ZWVliddee020bt1auLm5CRcXF9G6dWuxcOFCi65tsGDBAgFAdOrUyWxfZmameOWVV0TdunWFk5OT6Natm4iOjjYbytWS4WaFEOL27dti0qRJwtvbW7i4uIiBAweKixcvmsXk1q1bYuzYsaJ27drC1dVV9OnTR5w8eVIEBgaaDeO5ePFiERwcLGxsbEx+L3eXUQghrl27Zryuvb29CAsLMylzwfdS1t9dScN1GoZWPXv2rBg2bJjw9PQUjo6OolOnTuK3334zudZXX30lunfvLry9vYWDg4No1KiReO2110RycrIQQv0zMHfuXOHq6moy3Oe5c+fElClTRJcuXYSvr6+wtbUVPj4+YsCAAeLPP/80Od8wNOrdivqbCgwMFAMGDDDZZkkchLDsdyeEEDt37hTt27cX9vb2Jr+vospa2HMqhBCLFi0S7du3F05OTsLNzU2EhYWJ119/XVy+fNl4TF5enpg+fbrx76Nnz57i6NGjhT6ntWvXFl26dDG7z92KKqcQMlY2NjYm1/78889FaGiosLOzE35+fuL5558Xt27dMrvm3c+hu7u7eOCBB8SWLVtMji3p+S34/Bf2+zRYvny52b8JQhQ+3OySJUtEkyZNhIODgwgNDRXLli0r9PeSlJQk7O3txddff13oPYlqAkUIDXuiERERlVFycjKCg4Px8ccf46mnntK6ODXW8ePH0aJFC/z2228WNVGiws2bNw8ff/wxzp49W2z/F6LqjH0siIioWvLw8MDrr7+O2bNnV9poW9Zo69atCA8PZ1KhQk5ODubOnYt33nmHSQXVaKyxICIiIiIi1VhjQUREREREqjGxICIiIiIi1ZhYEBERERGRakwsiIiIiIhINaubIE+v1+Py5ctwc3ODoihaF4eIiIiIqMoSQiA1NRX+/v4mE90WxuoSi8uXLyMgIEDrYhARERERVRsXL15E/fr1iz3G6hILNzc3ADI47u7uFp+n1+uRmJgIHx+fErM1Msf4qcP4qcP4qcP4qcP4qcP4qcP4qcP4ASkpKQgICDB+hi6O1SUWhuZP7u7upU4sMjMz4e7ubrUPlhqMnzqMnzqMnzqMnzqMnzqMnzqMnzqMXz5LuhBYd4SIiIiIiKhcMLEgIiIiIiLVmFgQEREREZFqVtfHgoiIiKg60uv1yM7OLvU5OTk5yMzMtPo+AmVhDfGzs7ODjY1NuVyLiQURERFRFZednY3z589Dr9eX6jwhBPR6PVJTUzl/VxlYS/w8PT1Rp04d1e+RiQURERFRFSaEwJUrV2BjY4OAgIBSfXMuhEBubi5sbW1r9AfjilLT4yeEQEZGBhISEgAAdevWVXU9JhZEREREVVhubi4yMjLg7+8PZ2fnUp1b0z8YVzRriJ+TkxMAICEhAb6+vqqaRdXMxmJERERENUReXh4AwN7eXuOSUE1lSFhzcnJUXYeJBREREVE1UFO/MSftldezxcSCiIiIiIhUY2JBRERERNVCUFAQ5s2bp3UxqAhMLIiIiIioXCmKUuwybdq0Ml137969eOaZZ1SVrWfPnnjppZdUXYMKx1GhiIiIiKhcXblyxbi+evVqTJkyBbGxscZtrq6uxnUhBPLy8mBrW/LHUh8fn/ItKJUr1lgQERERUbmqU6eOcfHw8ICiKMbXJ0+ehJubG/744w+0b98eDg4O+Oeff3D27FkMHjwYfn5+cHV1RceOHbFlyxaT697dFEpRFHz99dcYMmQInJ2d0aRJE/z3v/9VVfZffvkFLVq0gIODAxo2bIhPPvnEZP/ChQvRpEkTODo6ws/PD8OGDTPu+/nnnxEWFgYnJyd4e3sjIiIC6enpqspTnTCxqMb0emD+fCAyEkhN1bo0RERERJZ788038eGHH+LEiRNo1aoV0tLS0L9/f0RFReHgwYPo27cvBg4ciPj4+GKvM336dAwfPhyHDx9G//79MXLkSNy8ebNMZdq/fz+GDx+ORx99FEeOHMHUqVMxbdo0LF++HACwb98+TJo0CTNmzEBsbCw2bNiA7t27A5C1NI899hiefPJJnDhxAtu2bcPQoUMhhChTWaojNoWqpnJzgSefBFaskK/z8oBPP9W2TERERFQ5OnQArl619Ojy+7hXpw6wb1/5XGvGjBno1auX8XWtWrXQunVr4+v33nsPa9euxX//+19MmDChyOuMGTMGjz32GABg5syZ+Oyzz7Bnzx707du31GWaO3cuHnjgAbz77rsAgCZNmuDo0aOYM2cOxo4di/j4eLi4uODBBx+Em5sbAgMD0bZtWwAyscjNzcXQoUMRGBgIAAgLCyt1GaozJhbVUGYm8NhjwLp1+du++QaYNQso5YScREREVA1dvQr8+68lR1bduS86dOhg8jotLQ3Tpk3D77//bvyQfvv27RJrLFq1amVcd3Fxgbu7OxISEspUphMnTmDw4MEm27p27Yr58+cjLy8PvXr1QmBgIIKDg9G3b1/07dvX2AyrdevWeOCBBxAWFoY+ffqgd+/eGDZsGLy8vMpUluqIiUU1k5YGPPQQEBVluj05GVi9Ghg7VpNiERERUSWqU8fSIws2w1GfZFh+35K5uLiYvH711VexefNmzJkzB40bN4aTkxOGDRuG7OzsYq9jZ2dn8lpRFOj1+vIraAFubm44cOAAtm3bhk2bNmHKlCmYNm0a9u7dC09PT2zevBk7d+7Epk2bMH/+fLz99tvYvXs3GjZsWCHlqWqYWFQjN28CAwYAu3bJ187OwNtvywUAvvqKiQUREZE1sLQ5khBAbm4ubG1tUdUn7t6xYwfGjBmDIUOGAJA1GHFxcZVahmbNmmHHjh0m23bu3ImmTZvCxsYGAGBra4uIiAhERERg6tSp8PT0xJ9//omhQ4dCURR069YN3bp1w5QpUxAYGIi1a9ciMjKyUt+HVphYVBNXrwK9ewNHjsjXnp7A+vVAly6ypuLwYWD3buDQIaBA80QiIiKiaqFJkyZYs2YNBg4cCEVR8O6771ZYzUNiYiJiYmJMttWtWxevvPIKOnbsiPfeew8jRozAzp078cUXX2DBggUAgN9++w3nzp1D9+7d4eXlhfXr10Ov1yMkJAS7d+9GVFQUevfuDV9fX+zevRuJiYlo1qxZhbyHqoijQlUDcXHAPffkJxW+vsC2bUB4OKAowLPP5h/71VdalJCIiIhInblz58LLywtdu3bFwIED0adPH7Rr165C7rVy5Uq0bdvWZFm8eDHatWuHH3/8EatWrULLli0xdepUTJ06FWPGjAEAeHp6Ys2aNbj//vvRrFkzfPnll/jhhx/QokULuLu74++//0b//v3RtGlTvPPOO/i///s/9OvXr0LeQ1WkCGsaAwtASkoKPDw8kJycDHd3d4vP0+v1SEhIgK+vL3S6ysvHTpwAevXK76DVoAGwZQvQpEn+McnJgL8/kJEBuLkBly8DBeadqRK0il9Nwfipw/ipw/ipw/ipw/gBmZmZOH/+PBo2bAhHR8dSnSuEKNAUqoq3haqCrCV+xT1jpfnsbJ1/odXEgQNA9+75SUVICPDPP6ZJBQB4eMhRogA5n8WqVZVbTiIiIiIiJhZV1PbtwH33Adevy9dt28ptAQGFH8/mUERERESkJSYWVdD69bKjdkqKfH3PPcDWrYCPT9HndOggkw9AjhRx4EDFl5OIiIiIyICJRRWzejUweLCcBA8A+vYFNm6UzZ2Kw07cRERERKQlJhZVyOLFsq9Ebq58/cgjwK+/Wj6b9uOP53faXrlS9rcgIiIiIqoMTCyqiDlzgGeekRPZAMDTTwM//ADY21t+DTc3mVwAcobulSvLv5xERERERIVhYqExIYB33gFeey1/2yuvAIsWAXcmeCyVgs2hFi1SXz4iIiIiIktUicRiwYIFCAoKgqOjIzp37ow9e/YUeWzPnj2hKIrZMmDAgEoscfnQ64GJE4EPPsjf9v77wOzZss9EWbRrJztyA7ID97596stJRERERFQSzROL1atXIzIyElOnTsWBAwfQunVr9OnTBwkJCYUev2bNGly5csW4HD16FDY2NnjkkUcqueTq5OQAo0cDd2aIBwDMnw+8/XbZkwoDduImIiIiosqmeWIxd+5cjBs3DmPHjkXz5s3x5ZdfwtnZGUuXLi30+Fq1aqFOnTrGZfPmzXB2dq5WiUVmJjBsGPDdd/K1jQ3w7bfAhAnlc/1HH5X9LQDZT8MwbC0RERFRddKzZ0+89NJLxtdBQUGYN29esecoioJ169apvnd5Xcea2Gp58+zsbOzfvx+TJ082btPpdIiIiEB0dLRF11iyZAkeffRRuLi4FLo/KysLWVlZxtcpdz5l6/V66PV6i8uq1+shhCjVOYVJTQWGDlXw55+yWsLeXuCHHwQeekg2jSoPzs7AyJEKvvxSQXo6sGKFHs8/Xz7XLqvyip+1YvzUYfzUYfzUYfzUYfzyY2BYSstwTlnOLatBgwYhJycHf/zxh9m+7du3o0ePHoiJiUGrVq1KvFbB971nzx64uLiU+F5KE6tp06bh119/xcGDB022X758GV5eXhUav+XLl+Pll1/GrVu3yv3apWGIV2Gfj0vzt6dpYnH9+nXk5eXBz8/PZLufnx9OnjxZ4vl79uzB0aNHsWTJkiKPmTVrFqZPn262PTExEZmGySIsoNfrkZycDCEEdLqyVfQIAYwY4YXt2x0AAM7OeixfnoSuXbNRRMuvMhs2zBZfflkbAPDFF3kYOvSG6iZWapRH/KwZ46cO46cO46cO46cO4wfk5ORAr9cjNzcXuYYx6S0khEBeXh4A+Q18ZRk9ejRGjBiBuLg41K9f32Tf0qVL0b59ezRv3rzE92P4wGs4zsvLCwBKPC8vL8/iWBkSt7uPr127doXHz/ChvbS/1/KWm5sLvV6PGzduwM7OzmRfainmL9A0sVBryZIlCAsLQ6dOnYo8ZvLkyYiMjDS+TklJQUBAAHx8fODu7m7xvfR6PRRFgY+Pj6p/2KZMAR58UMDZGfjtN6BLF88yX6s4vr5A584Cu3crOHbMDnFxvujcuUJuZZHyip+1YvzUYfzUYfzUYfzUYfyAzMxMpKamwtbWFra2ZfvodveHxYo2ePBg+Pj44LvvvsM777xj3J6WloZffvkFH3/8MZKTkzFx4kT8/fffuHXrFho1aoTJkyfjscceMx5vGKTH8L4bNmyIF1980dg86vTp03j66aexZ88eBAcHG5tJ2djYGM954403sG7dOly6dAl16tTB448/jilTpsDOzg7Lly/H+++/DwCwvzPG/9KlSzFmzBjodDqsWbMGAwYMgJ2dHY4cOYKXXnoJ0dHRcHZ2xtChQzF37ly43plEbOzYsUhKSkK3bt0wd+5cZGdnY8SIEZg3b16R8Tc800X9XuPj4zFp0iRERUVBp9Ohb9+++Oyzz4xfyh86dAgvv/wy9u3bB0VR0KRJE3z55Zfo0KEDLly4gIkTJ+Kff/5BdnY2goKC8PHHH6N///5m97G1tYVOp4O3tzccHR1N9t39ujiaJha1a9eGjY0Nrl27ZrL92rVrqFOnTrHnpqenY9WqVZgxY0axxzk4OMDBwcFsu06nK/U/UIqilOm8giIigF9+ARo0AMLCKvabg2efBXbvluuLF+sQHl6htytRecTPmjF+6jB+6jB+6jB+6lh7/HQ6nclImKUhhDCeU5k1FnZ2dhg1ahS++eYbvPPOO8Z7//zzz8jLy8Pjjz+OtLQ0tG/fHm+88Qbc3d3x+++/Y9SoUWjcuLHJl8Z3v2/Da71ej4cffhh+fn7YvXs3kpOTjQlHwXPc3d2xfPly+Pv748iRIxg3bhzc3d3x+uuv49FHH8WxY8ewYcMGbNmyBQDg4eFhdr/09HT07dsX4eHh2Lt3LxISEvD0009j4sSJWL58ufHYrVu3om7duti6dSvOnDmDESNGoG3bthg3blyhcSrud6PX6/HQQw/B1dUVf/31F3JzczF+/Hg8+uij2LZtGwDgP//5D9q2bYsvvvgCNjY2iImJgb29PRRFwYQJE5CdnY2///4bLi4uOH78ONzc3Aq9lyFehf2dlebvTtPEwt7eHu3bt0dUVBQeeughADKIUVFRmFBCT+affvoJWVlZ+M9//lMJJS1flTUy7vDhwEsvyc7bq1YBc+cCnp6Vc28iIiKqQBs6ALevWnRouX7Yc6oD9LVsLPsnn3wSs2fPxl9//YWePXsCAJYtW4aHH34YHh4e8PDwwKuvvmo8fuLEidi4cSN+/PHHYlujGGzZsgUnT57Exo0b4e/vDwCYOXMm+vXrZ3JcwRqToKAgvPrqq1i1ahVef/11ODk5wdXVFba2tsV+qb1y5UpkZmbi22+/Nfbr/fzzzzFw4EB89NFHxhoELy8vfP7557CxsUFoaCgGDBiAqKioIhOL4kRFReHIkSM4f/48AgICAADffvstWrRogb1796Jjx46Ij4/Ha6+9htDQUABAkyZNjOfHx8fj4YcfRlhYGAAgODi41GUoLc2bQkVGRmL06NHo0KEDOnXqhHnz5iE9PR1jx44FAIwaNQr16tXDrFmzTM5bsmQJHnroIXh7e2tR7GrBxQV44gk5pO3t23IUqvIaeYqIiIg0dPsqcPvfEg/TsHslQkND0bVrVyxduhQ9e/bEmTNnsH37dmNrk7y8PMycORM//vgj/v33X2RnZyMrKwvOzs4WXf/EiRMICAgwJhUAEF5I84zVq1fjs88+w9mzZ5GWlobc3NxSNYc33Kt169YmgwV169YNer0esbGxxsSiRYsWsCkww3HdunVx5MiRUt2r4D0DAgKMSQUANG/eHJ6enjhx4gQ6duyIyMhIPP3001ixYgUiIiLwyCOPoFGjRgCASZMm4fnnn8emTZsQERGBhx9+2KLO8mpoXqc4YsQIzJkzB1OmTEGbNm0QExODDRs2GH9B8fHxuHLlisk5sbGx+Oeff/DUU09pUeRq5e45LSpxQAgiIiKqKE51AKd6JS6iwGLJ8SUvxTdVv9tTTz2FX375BampqVi2bBkaNWqEHj16AABmz56NTz/9FG+88Qa2bt2KmJgY9OnTB9nZ2eUWpujoaIwcORL9+/fHb7/9hoMHD+Ltt98u13sUdHdfCkOTrYoybdo0HDt2DAMGDMCff/6J5s2bY+3atQCAp59+GufOncMTTzyBI0eOoEOHDpg/f36FlQWoAjUWADBhwoQimz4Z2pAVFBISUqlDplVnYWFAeDgQHQ0cPSp/du2qdamIiIhIFQubI+HOaEe2trbqZ+Atg+HDh+PFF1/EypUr8e233+L55583tvHfsWMHBg8ebGzWrtfrcerUKTRv3tyiazdr1gwXL17ElStXULduXQDArl27TI7ZuXMnAgMD8fbbbxu3XbhwweQYe3t748hPxd3rm2++QXp6urHWYseOHdDpdAgJCbGovKVleH8XL1401locP34cSUlJJjFq2rQpmjZtipdffhmPPfYYli1bhiFDhgAAAgIC8Nxzz+G5557D5MmTsXjxYkycOLFCygtUgRoLqniciZuIiIi04OrqihEjRmDy5Mm4cuUKxowZY9zXpEkTbN68GTt37sSJEyfw7LPPmg3oU5yIiAg0bdoUo0ePxqFDh7B9+3aTBMJwj/j4eKxatQpnz57FZ599ZvxG3yAoKAjnz59HTEwMrl+/bjL/mcHIkSPh6OiI0aNH4+jRo9i6dSsmTpyIJ554wmzahNLKy8tDTEyMyXLixAlEREQgLCwMI0eOxIEDB7Bnzx6MGjUKPXr0QIcOHXD79m1MmDAB27Ztw4ULF7Bjxw7s3bsXzZo1AwC89NJL2LhxI86fP48DBw5g69atxn0VhYmFFRg+PL/T9o8/AhrPwUJERERW5KmnnsKtW7fQp08fk/4Q77zzDtq1a4c+ffqgZ8+eqFOnjnEwH0vodDqsXbsWt2/fRqdOnfD000/jgw8+MDlm0KBBePnllzFhwgS0adMGO3fuxLvvvmtyzMMPP4y+ffvivvvug4+PD3744Qezezk7O2Pjxo24efMmOnbsiGHDhuGBBx7A559/XrpgFCItLQ1t27Y1WQYOHAhFUfDrr7/Cy8sL3bt3R0REBIKDg7F69WoAckjdGzduYNSoUWjatCmGDx+Ofv36Gedvy8vLw/jx49GsWTP07dsXTZs2xcKFC1WXtziKsLI2RSkpKfDw8EBycnKp57FISEiAr69vtRzu7sUXgc8+k+uffgpMmlS596/u8dMa46cO46cO46cO46cO4yfnsTh//jwaNmxYqjkFABgnfrO1ta3U4WZrCmuJX3HPWGk+O1vnX6gVYiduIiIiIqpITCysRPPmwD33yPXjx4EdO7QtDxERERHVLEwsrAg7cRMRERFRRWFiYUWGDQNq1ZLrP/0E3LihbXmIiIiIqOZgYmFFHB2B0aPlelYW8O232paHiIiIiGoOJhZW5pln8tfZiZuIiKj6sLKBPKkSldfs4FVi5m2qPKGhQI8ewF9/AbGxwN9/y9dERERUNdnZ2UFRFCQmJsLHx6dUw55ay3CpFaWmx08IgezsbCQmJkKn08He3l7V9ZhYWKFnn5WJBSBrLZhYEBERVV02NjaoX78+Ll26hLi4uFKdK4SAXq+HTqerkR+MK5q1xM/Z2RkNGjRQPVcMEwsrNHQoULs2cP068Msv8mft2lqXioiIiIri6uqKJk2aICcnp1Tn6fV63LhxA97e3lY7waAa1hA/GxubcquRYWJhhRwcgDFjgDlzgOxsYPly4NVXtS4VERERFcfGxgY2NjalOkev18POzg6Ojo419oNxRWL8SocRslLjxuWvL1rETtxEREREpA4TCyvVtClw331y/fRpYOtWbctDRERERNUbEwsrxpm4iYiIiKi8MLGwYkOGAD4+cn3tWiAhQdvyEBEREVH1xcTCitnbA2PHyvWcHNmJm4iIiIioLJhYWLm7O3GX08SLRERERGRlmFhYucaNgYgIuX72LPDnn9qWh4iIiIiqJyYWxE7cRERERKQaEwvC4MGAn59cX7cOuHpV0+IQERERUTXExIJgZwc8+aRcz80Fli3TtjxEREREVP0wsSAAshO3osj1n37StixEREREVP0wsSAAQMOGQMuWcv3QISAtTdvyEBEREVH1wsSCjLp1kz/1emDPHm3LQkRERETVCxMLMuraNX99xw7tykFERERE1Q8TCzIqmFjs3KldOYiIiIio+mFiQUbBwfnDzkZHcxZuIiIiIrIcEwsyUpT8WovkZOD4cW3LQ0RERETVBxMLMsHmUERERERUFkwsyIRhZCiAiQURERERWY6JBZlo1w6wt5frHBmKiIiIiCzFxIJMODgAHTrI9TNngIQEbctDRERERNUDEwsyU7A5VHS0duUgIiIiouqDiQWZ4UR5RERERFRaTCzITHh4/jo7cBMRERGRJZhYkBk/P6BxY7m+bx+QlaVteYiIiIio6mNiQYUyNIfKygIOHNC2LERERERU9TGxoEJxojwiIiIiKg0mFlQoTpRHRERERKXBxIIK1bw54O4u13fsAITQtjxEREREVLUxsaBC6XT5o0NduwacP69teYiIiIioamNiQUVicygiIiIishQTCyoSJ8ojIiIiIksxsaAideokm0QBrLEgIiIiouIxsaAiubkBrVvL9SNHgJQUbctDRERERFUXEwsqlqE5lBDArl3aloWIiIiIqi4mFlQsduAmIiIiIkswsaBicQZuIiIiIrIEEwsqVoMGgL+/XN+1C8jL07Y8RERERFQ1MbGgYilKfnOo1FTg6FFty0NEREREVRMTCyoRm0MRERERUUmYWFCJOFEeEREREZWEiQWVqG1bwMlJrrPGgoiIiIgKw8SCSmRnB3TsKNfPnweuXNG2PERERERU9TCxIIuwnwURERERFYeJBVmEE+URERERUXGYWJBFunTJX2cHbiIiIiK6GxMLskjt2kBIiFw/cAC4fVvb8hARERFR1cLEgixmaA6VkwPs369tWYiIiIioamFiQRbjfBZEREREVBQmFmQxjgxFREREREVhYkEWCwkBatWS6zt3AkJoWx4iIiIiqjqYWJDFdDogPFyuX78OnD6tbXmIiIiIqOpgYkGlwuZQRERERFQYJhZUKpwoj4iIiIgKw8SCSqVjR8DGRq5zZCgiIiIiMmBiQaXi7Ay0bSvXjx8Hbt3StjxEREREVDVonlgsWLAAQUFBcHR0ROfOnbFnz55ij09KSsL48eNRt25dODg4oGnTpli/fn0llZYA0+ZQu3ZpVw4iIiIiqjo0TSxWr16NyMhITJ06FQcOHEDr1q3Rp08fJCQkFHp8dnY2evXqhbi4OPz888+IjY3F4sWLUa9evUouuXXjRHlEREREdDdbLW8+d+5cjBs3DmPHjgUAfPnll/j999+xdOlSvPnmm2bHL126FDdv3sTOnTthZ2cHAAgKCqrMIhM4MhQRERERmdOsxiI7Oxv79+9HREREfmF0OkRERCA6OrrQc/773/8iPDwc48ePh5+fH1q2bImZM2ciLy+vsopNAOrXBxo0kOu7dwO5udqWh4iIiIi0p1mNxfXr15GXlwc/Pz+T7X5+fjh58mSh55w7dw5//vknRo4cifXr1+PMmTN44YUXkJOTg6lTpxZ6TlZWFrKysoyvU1JSAAB6vR56vd7i8ur1egghSnVOTRYeriA+XkFGBnDwoB7t2xd/POOnDuOnDuOnDuOnDuOnDuOnDuOnDuOHUr13TZtClZZer4evry8WLVoEGxsbtG/fHv/++y9mz55dZGIxa9YsTJ8+3Wx7YmIiMjMzS3Xv5ORkCCGg02ne511zYWHOWL3aHQCwaVMaAgIyij2e8VOH8VOH8VOH8VOH8VOH8VOH8VOH8QNSU1MtPlazxKJ27dqwsbHBtWvXTLZfu3YNderUKfScunXrws7ODjaGiRQANGvWDFevXkV2djbs7e3Nzpk8eTIiIyONr1NSUhAQEAAfHx+4u7tbXF69Xg9FUeDj42O1D1ZBffoA77wj148ccYOvr2uxxzN+6jB+6jB+6jB+6jB+6jB+6jB+6jB+gKOjo8XHapZY2Nvbo3379oiKisJDDz0EQP7yoqKiMGHChELP6datG1auXAm9Xm/85Z46dQp169YtNKkAAAcHBzg4OJht1+l0pX5AFEUp03k1UZs2ck6LjAxg504FOp1S4jmMnzqMnzqMnzqMnzqMnzqMnzqMnzrWHr/SvG9NIxQZGYnFixfjm2++wYkTJ/D8888jPT3dOErUqFGjMHnyZOPxzz//PG7evIkXX3wRp06dwu+//46ZM2di/PjxWr0Fq2VrC3TuLNcvXpQLEREREVkvTftYjBgxAomJiZgyZQquXr2KNm3aYMOGDcYO3fHx8SZZUkBAADZu3IiXX34ZrVq1Qr169fDiiy/ijTfe0OotWLVu3YCtW+V6dDQQEKBteYiIiIhIO5p33p4wYUKRTZ+2bdtmti08PBy7ON1zlXD3RHnDh2tXFiIiIiLSlnU2FqNy0aVL/jonyiMiIiKybkwsqMy8vIAWLeT6wYNAerq25SEiIiIi7TCxIFUMzaHy8oC9e7UtCxERERFph4kFqdKtW/46m0MRERERWS8mFqRKwQ7cTCyIiIiIrBcTC1KlcWPAx0eu79wJ6PXaloeIiIiItMHEglRRlPxai1u3gNhYbctDRERERNpgYkGqsTkUERERETGxINXuniiPiIiIiKwPEwtSrUMHwM5OrrPGgoiIiMg6MbEg1Rwdgfbt5XpsLHD9urblISIiIqLKx8SCykXB5lDR0dqVg4iIiIi0wcSCygUnyiMiIiKybkwsqFyEh+evM7EgIiIisj5MLKhc1K0LNGwo1/fsAbKztS0PEREREVUuJhZUbgzNoTIzgZgYTYtCRERERJWMiQWVG06UR0RERGS9mFhQueFEeURERETWi4kFlZuWLQE3N7m+cycghLblISIiIqLKw8SCyo2NDdCli1y/fBmIj9e2PERERERUeZhYULlicygiIiIi68TEgsoVJ8ojIiIisk5MLKhcde4MKIpcZ2JBREREZD2YWFC5cncHwsLk+qFDQGqqtuUhIiIiosrBxILKnaE5lF4vZ+EmIiIiopqPiQWVO06UR0RERGR9mFhQuePIUERERETWh4kFlbuGDYE6deR6dLRsEkVERERENRsTCyp3ipJfa5GSAhw7pm15iIiIiKjiMbGgCsH5LIiIiIisCxMLqhDsZ0FERERkXZhYUIVo1w5wdJTrTCyIiIiIaj4mFlQh7O2Bjh3l+rlzwNWr2paHiIiIiCoWEwuqMJzPgoiIiMh6MLGgCmPagVvRriBEREREVOGYWFCFCQ/PX4+O1q4cRERERFTxmFhQhaldGwgJkev79wO3b2tbHiIiIiKqOEwsqEIZmkPl5Cg4dMhO28IQERERUYVhYkEVqmAH7n377LUrCBERERFVKCYWVKEKduDeu5c1FkREREQ1FRMLqlAhIUCtWnJ97157CKFteYiIiIioYjCxoAqlKPnNoW7d0uHUKW3LQ0REREQVg4kFVbiCzaF27NCuHERERERUcZhYUIUr2IE7OpoT5RERERHVREwsqMJ17AjY2cnOFTt3alwYIiIiIqoQTCyowjk5Ae3ayfWTJxXcuKFteYiIiIio/DGxoEoRHp6/Hh2tXTmIiIiIqGIwsaBK0bVr/jiz7MBNREREVPMwsaBKUbADNxMLIiIiopqHiQVVirp1gQYNcgEAe/cC2dkaF4iIiIiIyhUTC6o0HTvmAAAyM4GDBzUuDBERERGVKyYWVGk6dsyvpuCws0REREQ1CxMLqjSGGguA/SyIiIiIahomFlRpQkJy4e4uR4fasQMQooQTiIiIiKjaYGJBlcbGBujcWa5fvQrExWlaHCIiIiIqR0wsqFJ168b5LIiIiIhqIiYWVKk4nwURERFRzcTEgipVp06A7s5Tx5GhiIiIiGoOJhZUqdzcgNat5fqRI0BysrblISIiIqLywcSCKl23bvKnEMDu3dqWhYiIiIjKBxMLqnTsZ0FERERU8zCxoEpnqLEAmFgQERER1RRMLKjSBQQA9erJ9d27gdxcbctDREREROoxsaBKpyj5tRZpabITNxERERFVb0wsSBNsDkVERERUszCxIE0U7MDN+SyIiIiIqj8mFqSJ1q0BZ2e5zhoLIiIiouqPiQVpws4O6NxZrsfHA5cuaVseIiIiIlKnTInFxYsXcanAJ8E9e/bgpZdewqJFi8qtYFTzsTkUERERUc1RpsTi8ccfx9atWwEAV69eRa9evbBnzx68/fbbmDFjRrkWkGouduAmIiIiqjnKlFgcPXoUnTp1AgD8+OOPaNmyJXbu3Invv/8ey5cvL/X1FixYgKCgIDg6OqJz587Ys2dPkccuX74ciqKYLI6OjmV5G6Sx8PD8ddZYEBEREVVvZUoscnJy4ODgAADYsmULBg0aBAAIDQ3FlStXSnWt1atXIzIyElOnTsWBAwfQunVr9OnTBwkJCUWe4+7ujitXrhiXCxculOVtkMY8PYEWLeT6wYNAerqmxSEiIiIiFcqUWLRo0QJffvkltm/fjs2bN6Nv374AgMuXL8Pb27tU15o7dy7GjRuHsWPHonnz5vjyyy/h7OyMpUuXFnmOoiioU6eOcfHz8yvL26AqwNAcKi8PKKaiioiIiIiqONuynPTRRx9hyJAhmD17NkaPHo3WrVsDAP773/8am0hZIjs7G/v378fkyZON23Q6HSIiIhAdHV3keWlpaQgMDIRer0e7du0wc+ZMtDB89X2XrKwsZGVlGV+npKQAAPR6PfR6vcVl1ev1EEKU6hzKV1T8unQBFi2S+e2OHXr06KFF6ao+Pn/qMH7qMH7qMH7qMH7qMH7qMH4o1XsvU2LRs2dPXL9+HSkpKfDy8jJuf+aZZ+BsmJzAAtevX0deXp5ZjYOfnx9OnjxZ6DkhISFYunQpWrVqheTkZMyZMwddu3bFsWPHUL9+fbPjZ82ahenTp5ttT0xMRGZmpsVl1ev1SE5OhhACOh1H6S2touIXGmoDwAcAsHVrDp5++pZGJaza+Pypw/ipw/ipw/ipw/ipw/ipw/gBqampFh9bpsTi9u3bEEIYk4oLFy5g7dq1aNasGfr06VOWS1osPDwc4QV6/Xbt2hXNmjXDV199hffee8/s+MmTJyMyMtL4OiUlBQEBAfDx8YG7u7vF99Xr9VAUBT4+Plb7YKlRVPx8fABfX4GEBAUHDtijdm1fMLzm+Pypw/ipw/ipw/ipw/ipw/ipw/ihVIMklSmxGDx4MIYOHYrnnnsOSUlJ6Ny5M+zs7HD9+nXMnTsXzz//vEXXqV27NmxsbHDt2jWT7deuXUOdOnUsuoadnR3atm2LM2fOFLrfwcHB2NG8IJ1OV+oHRFGUMp1HUlHx69oVWLcOSEpSEBuroIhWbVaPz586jJ86jJ86jJ86jJ86jJ861h6/0rzvMkXowIEDuPfeewEAP//8M/z8/HDhwgV8++23+Oyzzyy+jr29Pdq3b4+oqCjjNr1ej6ioKJNaieLk5eXhyJEjqFu3buneBFUZnM+CiIiIqPorU2KRkZEBNzc3AMCmTZswdOhQ6HQ6dOnSpdRDv0ZGRmLx4sX45ptvcOLECTz//PNIT0/H2LFjAQCjRo0y6dw9Y8YMbNq0CefOncOBAwfwn//8BxcuXMDTTz9dlrdCVQATCyIiIqLqr0xNoRo3box169ZhyJAh2LhxI15++WUAQEJCQqn6LQDAiBEjkJiYiClTpuDq1ato06YNNmzYYOzQHR8fb1IFc+vWLYwbNw5Xr16Fl5cX2rdvj507d6J58+ZleStUBbRrBzg4AFlZnCiPiIiIqLoqU2IxZcoUPP7443j55Zdx//33G5stbdq0CW3bti319SZMmIAJEyYUum/btm0mrz/55BN88sknpb4HVV0ODkCHDrK24swZ4No1gFOTEBEREVUvZWoKNWzYMMTHx2Pfvn3YuHGjcfsDDzzAD/1UJl275q8XM4UJEREREVVRZe7eXqdOHbRt2xaXL1/GpUuXAACdOnVCaGhouRWOrAf7WRARERFVb2VKLPR6PWbMmAEPDw8EBgYiMDAQnp6eeO+996x6ZkIqu4I1FkwsiIiIiKqfMvWxePvtt7FkyRJ8+OGH6Hbnq+Z//vkH06ZNQ2ZmJj744INyLSTVfD4+QJMmwOnTwP79QGYmUIr5WIiIiIhIY2VKLL755ht8/fXXGDRokHFbq1atUK9ePbzwwgtMLKhMunWTiUV2tkwuCjaPIiIiIqKqrUxNoW7evFloX4rQ0FDcvHlTdaHIOrGfBREREVH1VabEonXr1vj888/Ntn/++edo1aqV6kKRdSrYz4LzWRARERFVL2VqCvXxxx9jwIAB2LJli3EOi+joaFy8eBHr168v1wKS9QgNBby8gFu3ZGIhBKAoWpeKiIiIiCxRphqLHj164NSpUxgyZAiSkpKQlJSEoUOH4tixY1ixYkV5l5GshE4H3MlTkZgo+1sQERERUfVQphoLAPD39zfrpH3o0CEsWbIEixYtUl0wsk7dugGGSq+dO4GmTbUtDxERERFZpswT5BFVBHbgJiIiIqqemFhQldKxI2B7px6NHbiJiIiIqg8mFlSlODsDbdvK9ePHAY5eTERERFQ9lKqPxdChQ4vdn5SUpKYsRABkc6i9e+V6dDQwYIC25SEiIiKikpWqxsLDw6PYJTAwEKNGjaqospKV4HwWRERERNVPqWosli1bVlHlIDJiB24iIiKi6od9LKjK8fcHgoLk+p49QE6OpsUhIiIiIgswsaAqydAc6vZtICZG06IQERERkQWYWFCVxOZQRERERNULEwuqktiBm4iIiKh6YWJBVVJYGODmJtd37ACE0LY8RERERFQ8JhZUJdnYAF26yPXLl4ELF7QtDxEREREVj4kFVVlsDkVERERUfTCxoCqLHbiJiIiIqg8mFlRlde4M6O48oUwsiIiIiKo2JhZUZbm7y07cAHDkCJCSom15iIiIiKhoTCyoSjM0h9Lrgd27tS0LERERERWNiQVVaQU7cLM5FBEREVHVxcSCqrSCHbg5MhQRERFR1cXEgqq0wEDA31+u79oF5OVpWx4iIiIiKhwTC6rSFCW/OVRqKhATo2lxiIiIiKgITCyoyrv//vz133/XrhxEREREVDQmFlTlPfhg/vpvv2lXDiIiIiIqGhMLqvICAoDWreX63r3AlSvaloeIiIiIzDGxoGph4MD89fXrtSsHERERERWOiQVVC2wORURERFS1MbGgaqFjR8DXV65v2gRkZmpbHiIiIiIyxcSCqgWdDhgwQK5nZADbtmlaHCIiIiK6CxMLqjYKNof63/+0KwcRERERmWNiQdVGr16Avb1c/+03QAhty0NERERE+ZhYULXh5gb07CnX4+OBo0c1LQ4RERERFcDEgqoVNociIiIiqpqYWFC1wmFniYiIiKomJhZUrTRsCLRoIdd37QISErQtDxERERFJTCyo2jHMwi0E8Mcf2paFiIiIiCQmFlTtsDkUERERUdXDxIKqnS5dAG9vub5xI5CdrW15iIiIiIiJBVVDNjZA//5yPTUV+PtvbctDREREREwsqJrisLNEREREVQsTC6qW+vQBbG3l+v/+x1m4iYiIiLTGxIKqJQ8PoHt3uX7+PHDypLblISIiIrJ2TCyo2mJzKCIiIqKqg4kFVVuG+SwADjtLREREpDUmFlRtNW4MhITI9R07gBs3tC0PERERkTVjYkHVmqHWQq8HNmzQtixERERE1oyJBVVrnIWbiIiIqGpgYkHVWteugKenXP/jDyAnR9PiEBEREVktJhZUrdnZAf36yfXkZNnXgoiIiIgqHxMLqvY47CwRERGR9phYULXXty9gYyPX2c+CiIiISBtMLKjaq1UL6NZNrp86JRciIiIiqlxMLKhG4OhQRERERNpiYkE1AmfhJiIiItIWEwuqEUJCgEaN5Pr27UBSkqbFISIiIrI6TCyoRlCU/FqL3Fxg40Zty0NERERkbZhYUI3BfhZERERE2mFiQTXGvfcC7u5yff16WXNBRERERJWDiQXVGPb2QJ8+cv3mTWDXLm3LQ0RERGRNmFhQjcJZuImIiIi0USUSiwULFiAoKAiOjo7o3Lkz9uzZY9F5q1atgqIoeOihhyq2gFRt9O8vO3ID7GdBREREVJk0TyxWr16NyMhITJ06FQcOHEDr1q3Rp08fJCQkFHteXFwcXn31Vdx7772VVFKqDmrXBsLD5frx48C5c9qWh4iIiMhaaJ5YzJ07F+PGjcPYsWPRvHlzfPnll3B2dsbSpUuLPCcvLw8jR47E9OnTERwcXImlpeqAk+URERERVT5bLW+enZ2N/fv3Y/LkycZtOp0OERERiI6OLvK8GTNmwNfXF0899RS2b99e7D2ysrKQlZVlfJ2SkgIA0Ov10Ov1FpdVr9dDCFGqcyhfZcavf39g8mSZM//vfwITJogKv2dF4/OnDuOnDuOnDuOnDuOnDuOnDuOHUr13TROL69evIy8vD35+fibb/fz8cPLkyULP+eeff7BkyRLExMRYdI9Zs2Zh+vTpZtsTExORmZlpcVn1ej2Sk5MhhIBOp3lFT7VTmfHz8QHq1/fBpUs2+Osv4OzZRLi5Ve/kgs+fOoyfOoyfOoyfOoyfOoyfOowfkJqaavGxmiYWpZWamoonnngCixcvRu3atS06Z/LkyYiMjDS+TklJQUBAAHx8fOBumPTAAnq9HoqiwMfHx2ofLDUqO36DBytYsADIyVEQE+ODhx+u8FtWKD5/6jB+6jB+6jB+6jB+6jB+6jB+gKOjo8XHappY1K5dGzY2Nrh27ZrJ9mvXrqFOnTpmx589exZxcXEYWKARvaF6xtbWFrGxsWjUqJHJOQ4ODnBwcDC7lk6nK/UDoihKmc4jqTLjN3AgsGCBXF+/XodHHqnwW1Y4Pn/qMH7qMH7qMH7qMH7qMH7qWHv8SvO+NY2Qvb092rdvj6ioKOM2vV6PqKgohBuG9ikgNDQUR44cQUxMjHEZNGgQ7rvvPsTExCAgIKAyi09VWM+egIuLXP/9dyAvT9PiEBEREdV4mjeFioyMxOjRo9GhQwd06tQJ8+bNQ3p6OsaOHQsAGDVqFOrVq4dZs2bB0dERLVu2NDnf09MTAMy2k3VzcAB69wbWrgUSE4G9e4EuXbQuFREREVHNpXliMWLECCQmJmLKlCm4evUq2rRpgw0bNhg7dMfHx1tt1ROp8+CDMrEA5CzcTCyIiIiIKo7miQUATJgwARMmTCh037Zt24o9d/ny5eVfIKoRBgzIX//tN+CDD7QrCxEREVFNx6oAqrH8/IBOneT64cNAfLy25SEiIiKqyZhYUI3GWbiJiIiIKgcTC6rRHnwwf52JBREREVHFYWJBNVrr1kD9+nL9zz+B9HRty0NERERUUzGxoBpNUfJrLbKygC1btC0PERERUU3FxIJqPDaHIiIiIqp4TCyoxrv/fsDJSa7/9hug12tbHiIiIqKaiIkF1XhOTkBEhFy/ehU4cEDb8hARERHVREwsyCoUbA71v/9pVw4iIiKimoqJBVmFu2fhJiIiIqLyxcSCrEK9ekC7dnL9wAHg33+1LQ8RERFRTcPEgqxGwVm4f/9du3IQERER1URMLMhqcNhZIiIioorDxIKsRrt2QJ06cn3LFuD2bW3LQ0RERFSTMLEgq6HT5dda3L4NrF+vbXmIiIiIahImFmRVhgzJX3/zTSAzU7uyEBEREdUkTCzIqvTrB9x7r1w/cwaYPVvb8hARERHVFEwsyKooCrBgAWBjI1/PnAmcO6dtmYiIiIhqAiYWZHXCwoAXX5TrmZnApEmAENqWiYiIiKi6Y2JBVmnaNMDfX67//jvw3/9qWhwiIiKiao+JBVklNzfgk0/yX7/4IpCerl15iIiIiKo7JhZktR55BIiIkOsXLgAffKBteYiIiIiqMyYWZLUMHbnt7eXrOXOAkye1LRMRERFRdcXEgqxa06bAa6/J9ZwcYMIEduQmIiIiKgsmFmT13noLCAyU61FRwOrV2paHiIiIqDpiYkFWz9kZmD8//3VkJJCSol15iIiIiKojJhZEAAYOlAsAXLkih6MlIiIiIssxsSC649NPAScnuf7ZZ8Dhw9qWh4iIiKg6YWJBdEfDhsDbb8v1vDzg+ecBvV7bMhERERFVF0wsiAp49VU5UhQA7NwJfPONtuUhIiIiqi6YWBAV4OAAfP55/uvXXwdu3tSuPERERETVBRMLorv06gUMHy7Xr1/Pbx5FREREREVjYkFUiLlzAVdXuf7VV8DevdqWh4iIiKiqY2JBVIh69YDp0+W6ELIjd16etmUiIiIiqsqYWBAVYeJEoGVLub5/P7BokbblISIiIqrKmFgQFcHODli4MP/1W28BCQnalYeIiIioKmNiQVSMe+8FRo+W60lJcpQoIiIiIjLHxIKoBB9/DHh6yvVvvgG2b9e0OERERERVEhMLohL4+gIffJD/+oUXgJwc7cpDREREVBUxsSCywLPPAu3by/WjR4H587UtDxEREVFVw8SCyAI2NsAXXwCKIl9PnQr8+6+2ZSIiIiKqSphYEFmoY0dZcwEAaWlAZKS25SEiIiKqSphYEJXCBx8AtWvL9R9/BDZv1rY8RERERFUFEwuiUqhVS44SZTB+PJCVpV15iIiIiKoKW60LQFTdjB4NLFkC7NgBnD4NzJkDvP22+XF5eXLui1u38n8alqJep6UB7u4ygalVC/Dyyv9pY+OIwEBZY1Jwv7195b5/IiIiosIwsSAqJZ1Ozsjdrp1MHt5/H9i/3zxRSEkp17sC8Cx0j4tLfqJRMBmpVQuoWxcICMhf/Pxk+YmIiIjKGxMLqvrSzgH7XgQaPw349wd0dlqXCK1aARMnAvPmAZmZwNq16q+p0wHOzkB6OiCE5eelp8vl4sWSj7WzA+rVM0027l68vfNHvyIiIiKyFBMLqvrOLgUu/yYXxzpA8Big0VOAW2NNizV9OvDHH0BsbP42e3tZY+DlJWfrLmy9qNdubjK5yMsDkpOBmzdl7cfNm8D163rEx6chN9cVt27pcPOm6X7Dkp1dfJlzcoC4OLkUxckJqF/fPOEIDZVzebi4qI0cERER1URMLKjquxqVv555FTj+oVz87gMaPQ0EDAVsHCu9WO7uwKFD8kO6m5tMDpyc1H/bb2OT35TJQK8HEhIy4OvrWmRTJiGA27fzk4wbN+RcGxcvmi83bxZ9/9u3Zd+R06cLL1urVkB4ONCli1waN2YNBxERETGxoOqg1z/A1c3A2a+BS78CIlduv7ZVLvZeQNB/ZJLh1apSi+bgAISEVOoti6QosimVs7OscShOejpw6VLhSYdhSU01Py8vDzh4UC4LF8pt3t75SUZ4uJzvw929/N8fERERVW1MLKjq09kA/n3lcvsacP5bmWSknpL7s28Bp+bLpVZH2Rcj8FHAjp9ui+LiIhOi4pKi5OT8JOPCBZlMREcDR4+a9gG5cQP4/Xe5ADLBadHCtFYjNJSdxomIiGo6JhZUvTj5Ac1fA5q9CiT+IxOM+J+AvNty/829wJ69wP6XgcARshajdjjb6pSBh4dcWrY03Z6SAuzdC+zalb9cv56/XwiZfBw9CixenH+tzp3zazU6dGAncSIiopqGiQVVT4oC+N4rl/afAhd+AM58Ddw6IPfnZQDnlsnFo7lMMIKeABxra1vuGsDdHXjgAbkAMpE4ezY/yYiOln1P8vLyz0lOBjZtkouBhwfQqBEQHCx/Flzq15f9OYiIiKj6YGJB1Z+9J9DkebncPCBrMeK+B3LuTCSRfBw4EAnEvAHUHyKTjDoPAArb5pQHRZEduBs3Bv7zH7ktIwPYt8802bh61fS85GTgwAG53M3ODggKMk84GjUCGjaU/UiIiIioamFiQTVLrXZArYVA2zlA/M8yyUjcLvfpc4D4H+XiEgQEPwk0Ggs4l9DTmUrN2Rno3l0ugKzViI/PTzKOHZO1HPHxpjUbBjk5RY9MBciJ/wyJRuPGQLNmsl9Ho0YyKSEiIqLKx8SCaiZbZyB4lFxSYoGzS4Bzy4GsRLk/PQ44MgU4Og2o21fWYtR7sEpMvlcTKQoQGCiXESPyt+fkyOTi7Fnz5dw5OXpVYa5ckcs//5hut7OTHdJbtJBL8+byZ3Bwxb03IiIikphYUM3nHgK0/Rho9b6cZO/M18CVDQAEIPTA5fVycfSTk+8FPwW4N9G61FbBzi6/5uFuQgAJCUUnHdeumZ+Tk5Pfcbwge3sFjRp5IyxMQcuW+UlH48aALf8VJCIiKhf8L5Wsh429nEwvYCiQHi87dp9dCmTEy/2Z14DjH8nFt8edyfceBmydtC23lVIUwM9PLl27mu9PS5MJxqlTsmnV8ePy56lTMsEoKDtbwYkTdjhxAvjxx/zt9vb5NRzNm8smVU5O+cPpFhxW9+5tJf0EAFfX/MkOa9WSHdY57C4REdVUTCzIOrk0AMKmAi3eAa5ukX0x/v1V9sMAgIS/5LJvgpx8r/HTgFcbTYtMplxd5SzgrVoBw4blbzf0zzAkGnIROHUKyM01Hd82Oxs4ckQulUFRAC8v02TDksXLizUrRERU9fG/KrJuOhvAv49cMhOA8ytkkpFyUu7PSQZOL5BLrfayFiPwMcDeQ9tyU5Hs7GTtQ/Pm+QmHXi9w6VICUlJ8ceKEzphwHD+OOwlH5ZRNCODmTbmUVr16suama1egWzegTRt2VCcioqqFiQWRgaMv0OwVIDQSuL5TJhgXVheYfG+/XA5EAg2GA43HAbW7cpa3asLeXiYbLVsCjzySvz07W9ZwGEaqMoxSVfDXalgvbtvd+4SQzbUMicTdS1KSabOpkvz7L/DTT3IBZJOtjh1lkmFIOGrVsvx6ZSUEcPkycPIkEBsrO9HXq5c/H0mDBkx4iIisFRMLorspCuDTTS7t5gEXVgFnF8ukApCJxvlv5OIeKmsxGo4CHH00LTaVjb19/ihSlSkvT87lUVTiUXC5cUMmPqmp+effvg38/bdcDEJD8xONbt2Apk3LnvdmZMjanNhYmUQcOuSBCxcUnDolE6ai6HQyuTBMfmhYDK+9vMpWHiIiqvqYWBAVx94DaPKsXG4elMPWxn0nm0gBssnUwVeBQ5OBeoPvTL4XIZtYERXDxia/D4Ul8vLkaFc7dgA7d8qfcXGmx5w8KZclS+Rrb+/82oyuXWUNh1OBsQiEkDUhhtoHQxIRGyuHAc6nA2DZIAZ6vSxXXBwQFWW+39Oz6KQjIIB9SYiIqjNFiNJUxld/KSkp8PDwQHJyMtzd3S0+T6/XIyEhAb6+vtBxWJdSq1Hxy70NXPxFNpVK+Mt8v3MDoNGTcgI+l4ByuWWNip8Gamr8Ll+WSYZhOXDAfESsgmxtgXbt5HwiZ87IGomi5gopjE4n0LChgpAQGJd69WQ5DMMAnzsn15OTS/9+bG1lZ/xu3YB77pE/69Ur/XWqmpr6/FUWxk8dxk8dxq90n52ZWFiID5Y6NTZ+KaeAc0vl5HuZd0+soMjJ9xo/Dfg/KIe7LaMaG79KYi3xu30b2Lcvv1Zj507ZjKq0PD3zE4fQUKBJEz18fG6iU6dacHKyLH43b5omGob1c+dkbYheb1lZgoLyk4x77pH9ZKrbr9Banr+Kwvipw/ipw/iV7rMzK52J1HBvCrT5EGj1HvDvb7IW48oGOfEeBHDlD7k4+gINRwONnpIT9hFVACcn4N575QLIpk6nTuU3ndq5EzhxQu7T6WTzI0PyUDCR8PEx7Zuh1wMJCblwcLC8LIZmXh06mO/LzpbJxd1Jx8mTsnwFv+4yNKv67jv52tMzvw/JPfeYN+8iIiLtMLEgKg86OyBgiFzSL8oajHNLgPQLcn9mAnBitlx87pV9MRoMA2ydNS021WyKkp8wjB0rt924IZegINlxXQv29nLW88aNzfclJQHR0cA//8hkaPduIDPTdP/69XIB5AhU7dubNp/y4TgKRESaYGJBVN5cAoCwd4GWbwNXo2QtxqW1+ZPvJW6Xy/5JQNBImWTUaqttmclqeHvLpary9AT69ZMLIGs3Dh7MTzT++QdITMw/PicH2LVLLv/3f3Jb06YywejdGxg8mDUaRESVhYkFUUVRdEDdXnLJTCww+d6dtig5ycDphXLxaif7YgQ+Bth7alpsoqrE3h7o3Fkur7wim0mdOWOaaMTGmp5z6pRcli0D3N2BRx+VNTadO3PaGSKiilQleqEsWLAAQUFBcHR0ROfOnbFnz54ij12zZg06dOgAT09PuLi4oE2bNlixYkUllpaoDBx9gGaRwIBjQK8dQPBYwKZAM6hbB4C9LwBr/YHo0UDC9tLNnkZkJRQFaNJEJgpffy37ZSQkAOvWAa+9BoSHm07Ql5ICLFoktzdvDnz0kRzFioiIyp/micXq1asRGRmJqVOn4sCBA2jdujX69OmDhISEQo+vVasW3n77bURHR+Pw4cMYO3Ysxo4di40bN1ZyyYnKQFEAn65Al6XA0CtAp6+AWh3z9+fdBs5/C2zpDvwWChyfDdy+e7QpIirIx0c2efr4Y9lBPTkZ2LIFGDMGcHHJP+7kSeDNN+V8Gf36AT/+aNp/g4iI1NF8uNnOnTujY8eO+PzzzwHIYb0CAgIwceJEvPnmmxZdo127dhgwYADee++9Eo/lcLPaYPxKcOuQnHzv/AogJ8l0n2ILUW8Qbnk/DM+QR6CztSv0ElQ0Pn/qVOf4paUBv/wim0X9Vci0M15ewGOPySSkQ4eKaSpVneNXFTB+6jB+6jB+pfvsrGmEsrOzsX//fkRERBi36XQ6REREIDo6usTzhRCIiopCbGwsunfvXpFFJapYXq2BDp8BQy4DXb8HfHvm7xO5UC6tQa1DI6H81gg4PC1/tCkiKparKzB6NLBtmxzadupUOUGgwa1bwMKFQKdOQFgYMGcOcPWqZsUlIqrWNO28ff36deTl5cHPz89ku5+fH06ePFnkecnJyahXrx6ysrJgY2ODhQsXolevXoUem5WVhaysLOPrlJQUADID1Vs6Q9Od44UQpTqH8jF+FtI5AA0elUvqaSjnlgHnv4GSKT/pKBkXgaPTIY7OAOr0gmj0FOA/SNXke9aAz586NSV+QUHAlCnAO+/I2ovlyxX88gtw+7aspjh2TPbTePNNgT59gDFjBB58EKWav6MwNSV+WmH81GH81GH8UKr3Xi1HhXJzc0NMTAzS0tIQFRWFyMhIBAcHo2fPnmbHzpo1C9OnTzfbnpiYiMxSNK7V6/VITk6GEMJqq8LUYPzKwgOo+xLgNx7217fALv5buKb8DQV6KBDA1U1Qrm5Cnp03Mus8ggz/x5Dn0lTrQldJfP7UqYnxa9ECmD0bmDJFwW+/OWLVKifs2SMT9Lw85c5cGQq8vPQYOvQ2Roy4jbCw3DLdqybGr6IIAfz7rw7Hjtnh2DFbHD0qfyYk+MHWFrCxkYutrbjzWv7U6Qpuk+vypzym4DlOTgItWuSiXbtstGmTC2fnmj1QBp8/dRg/IDU11eJjNe1jkZ2dDWdnZ/z888946KGHjNtHjx6NpKQk/PrrrxZd5+mnn8bFixcL7cBdWI1FQEAAbt26Veo+FomJifDx8bHaB0sNxk8dY/xcsqG7sALKuaVQ0s+bHSdqd4MIfhJo8Ahg61LIlawTnz91rCV+Z84A336r4NtvgYsXzTtbdOki8MILAsOGla4Ww1riV1o5ObJDfUwMcOiQgkOH5PrNm5U3JrCNjUBYmGFIY4EuXeSoYzXp18TnTx3GT3529vLysqiPhaY1Fvb29mjfvj2ioqKMiYVer0dUVBQmTJhg8XX0er1J8lCQg4MDHAr5H0Cn05X6AVEUpUznkcT4qaMoCnSuAdCFvQO0fAu49idwxjD5XrY85voOKNd3AAdfBgIfl3NjeLXj4P3g86eWNcSvaVPg/feB6dOBrVtlh+81a/JHjtq1S8GuXQpefRUYNw549lk5wpQlrCF+xUlJAQ4flomDYTl6FCjiv24Tzs4CAQG5sLGxRW6ugtxcIDcXyMuDcb2wbZbIy1OM5fnqK/nvpJeXTDS6dJFLp05yW3Vm7c+fWtYev9K8b82bQkVGRmL06NHo0KEDOnXqhHnz5iE9PR1jx44FAIwaNQr16tXDrFmzAMimTR06dECjRo2QlZWF9evXY8WKFfjiiy+0fBtElUvRAXUi5JJ5HYj7Tk6+l3xM7s9JAc58KRevNnJ276DHAftq/r8jUSWwsQEiIuSSnAysWiU7eB8+LPcnJAAffAB8+CHw0EPA+PFAz57M3w2EAP7+W05eaPjQfuaMZefWqQO0aQO0bSt/tmkDNGwocOPGjTuj8lgWZCEAvb7wBCQxEdizJ3/G9qNH5bEGt24BGzbIxSA0ND/R6NJFNqWz1fwTFFHVo/mfxYgRI5CYmIgpU6bg6tWraNOmDTZs2GDs0B0fH2+SKaWnp+OFF17ApUuX4OTkhNDQUHz33XcYMWKEVm+BSFuOtYHQl4CQF4Ebu2WCcWEVkJsu99+KAfZNAA6+CgQMk0mGb3d+CiKygIeHrJl45hk50/fnn8vhaw0fWH/5RS7NmwMTJgD/+Q/g5qZ1qbVz4ICcIX3btuKPUxRZQ1QwgWjdWiYWdytLn1lFye+PcTdfX5kY3Pn+EqmpwL59+YnGrl0yeSzo5Em5LF8uX7u4AB07yiTDMDN83bqlLydRkfR5QNo5IPkIkJMKNBgO2DppXaoSaT6PRWXjPBbaYPzUKXX8clKBC6tlknFjt/l+tyYywWg4GnDyM99fw/D5U4fxM3XlipzN+8svzYemdXOTc2KMHw+EhMht1hC/f/8F3n4b+PZbWVtQkJMT0KpVfgLRpo0c2tfFwm5glR0/IYC4ONNE4+BB2SekOA0a5CcaXbrIpMmpCnwOtIbnryJVSvwyr8sEIukIkHQYuHVYtkDIy8g/pukEoMP8irl/CUrz2ZmJhYX4h6kO46eOqvglHZF9MeJWANm3TPcptkC9gTLJqNsH0BXy9V4NwOdPHcavcNnZwNq1shbjn3/M9/fqJROM/v31uHGjZsYvPV2OrjV7NpBR4DNQo0bAW28B4eGyZqKwmgNLVYXnLzNTJhcFk434+OLPsbWVSZQh0ejcGWjcuPIri6tC/Kqzco1fXhaQciI/gTD8vH2l5HPr9gHu21DycRWAiUUxmFhog/FTp1zil5cJXFwrazGu/Wm+37k+EDwWCH4ScA1SVd6qhs+fOoxfyQ4dAhYsAL77Drh923RfYKDAf/6ThkmTXODrWzPip9fL2om33pI1OAaennKukPHjAftyml6nqj5/ly/LBGP3brns3WuaXBWmVi3TRKMyOoZX1fhVF2WKnxBARrx5ApESC4g8Cy6gAK6NAPcQ4PLvchMTi6qJiYU2GD91yj1+qWeBc0uBc8sK+aZEkZ3CGz0N1B8M2KicHawK4POnDuNnuVu35GhSCxYA586Z7nNwEHj0UQXjxwMdOlTfbk5bt8p+FAcP5m+ztQVeeEEmFd7e5Xu/6vL85ebKSRYNycauXcCJEyWfFxKSn2iEh8tmYmpqeO5WXeJXVZUYv5yUO4mDIYk4DCQdBXKSLbuBfS3As9WdJezOzxaArQvybifDZq0nAOCGfR94D2NiUeUwsdAG46dOhcVPnwtc/kPWYlz+3fybFAdvIGiUHLbWo3n53beS8flTh/ErPb0e2LhRNpP64w8BIUyziNBQYPhwYMQI2fG7Ojh1Ss5M/t//mm4fPBj4+GPZ5KkiVOfnLylJ1mQYEo3du4Hr14s/x91dJhj33COXTp0AZ+eyl6E6x68qMMavdi3o0s+Y10KkX7DsQjo7wL15geThTiLhVNfsW4akJGDpUuCbxck49K4nAODAlT5o9woTiyqHiYU2GD91KiV+GZeB88uBs0vkSBR3c2kIONUBHH0BB1/A0efOzzuLg8+dn7UBneYDzpng86cO46fO6dN6zJ17G6tWOSMpybyaomXL/CSjoj6cq3HjBjBjhhxyt+D8EG3aAHPnAvfdV7H3r0nPnxCyJqtgolFSx3BbW6B9+/xEo1s3wMfH8nvWpPhVCiGAzKvGxEHcOozc6wdhm3Eait6CiVcAwLlBgQTizk/3pjK5KMaJE8D8+cA338hmde5OyUj+2hMAsP1MH3R4bYMmAwIwsSgGEwttMH7qVGr8hB64tk3WYlz8xTj5XqnY1yqQcNydhNyVkNh7yXk5KhCfP3UYP3UM8XN19cWqVTqsWAFs324+ehIgP6wbkozg4EovqonsbNmka8YM+Q2qQd26wMyZwBNPlG+TnaLU9OcvM1PO9REdDezcKZ+Na9eKP6dp0/xE4557iu8UXtPjp0puhhx96e5aiKwSqpUMbN3MEwjPloC9p8VF0OuB9euBzz4DNm823VcwsRB1+kC5nzUWVQ4TC20wfupoFr+sG0Dc98D574C0M+ajSpUHxUbWcpjUehSRhDj4AHbupW6czudPHcZPncLi9++/wM8/Az/+KD9MFqZDB5lkDB8OBAZWXnmFkKNdvf46cPZs/nYnJ7nttdcsHyq2PFjb82eo1fjnn/zl5Mniz/H1NU002rQB7O58OW5t8SuU0Mua+LsTiNQzAEr+GCwUG8CtKRSTBCIMcAksc2eppKT8/lgF/84A+fc1Zgww6flkND3kKTey83bVxMRCG4yfOlUmfnnZ8pucrEQgM0EuWQlAZuKdn4Ztd/bnppV/GXT2JSchhn2OvoCtc9WJXzXF+KlTUvzi44GffpJJxp49hV+jSxdZizFsGFC/fsWVdd8+IDJSfmtuoCjAqFFytvF69Sru3kXh8yf7ZezcmZ9o7NtXfPMpZ2fZj8fNDXBxEbCzy4S3tyNcXRW4usK4uLgUvl7wdbWbYTzrhnkCkXwsf9LYkjjWMUke9B4tkZDpDd+6Dcrl+Tt5Mr+5U/pdRQoOBiZOlJM3engAyE4GfvaUO6tJYlHdHhci0pKNPeDsLxdL5N62PAnJTAAsab+qzwYyLsnFErYuUBx8UMvGC4qrP+DoV3QS4uAj3yNRJWrQQI6y9MorwPnzMsFYvdp01CXD3Akvvyy/kTYkGYXNVG0ghGxmk5ZmuqSnm29LS5Ptu3/80fQaPXrIfhTt2lXMeyfL1K4NDBokF0AOabx3b36isXMnkFxgEKKMDDkLuqQAKHvDfAcHOdJXhw6yU3l4uFyvzFqrQuVlASknC5kT4rJl59s4AR4t7mrGFCb/fyhIrzefir2U9Hrgjz9kc6dNm8z39+oFTJoE9OtXOc0LKxJrLCzEb0zUYfzUsYr4CSFrOCxNQrISLRwPvJTsPArpkF5EEuLgXeU6qleEGvf86fNkEqvPkh9OCvupzzbdZuMgO2S6NJB9iErR/KGs8Tt9Oj/JOHLEfL+iyI68bm5FJw56vcW3M9GkiZz0btAg7YfFrXHPXwXQ6+VQtwWbT126VPbff0lsbORs6oZEo0sXOSlihTwrQgAZF4uYEyK35PMBOSfE3QmEayOLJoVV8/wlJwPLl8vR4c6cMd3n4iJrAidMKGZkONZYEBGVkaIAdm5ycWtU8vFCD2QnWZSEiMwEIPsmFAva0iInWS6ppy0ptEwuLG2aZe9Z4R3Vq5xCP8Rnl/zBvtCf2UUkAaU8T21CauMsEwxDomH46RIofzrVL5earyZNgLfflouhNmH16vy5EYQofMZvNby8gKlTgeefL78J7qji6XRy/ouwMPm7A/JrrFJT9bhw4QYcHLyRkaEzSTzvrr0q6vWFC6Yd+PPyZI3awYNytDBAjlTVpUt+otGxo2xKVSo5KXIOiIIJRNKRUs4JcVdnao8WgF1pC1J2WVnyi4BvvpFJRdpdLYIbNsxv7uTpWWnFqjRMLIioelJ0gEMtuSC02EOFXo+Eq5fh62kDXfZ1mYQYE5K7akIMry36j0zc6XNi4Qgiiu2dhKNgElJMrYita+m+AjR+iM9W/+G7wE8lLxMeGclQTiuAuPvaRZ9XLh/iq6K8DNkEI6WoHrWKHJr5TsKhOAXAWe8FZDcHXBuWqdajWTP5gX/KFODoUZlgrF5t+i2ojY2svSjYTr6wtvNF7XNzk51+S/1hkKokRZEd7h0cAL0+D76+MgEpC71ezmMSHZ2/HDtmOrJZYiLwv//JBZD3MtRqGBIO4+hV+lz55Y3ZnBBxlhVIZwe4NzOvhXDyr9QqtpQU4NCh/CTr4EHg+PHC+79ERMjmTv37V//mTsVhYkFE1kFnKz+sO9e17Pi8rAIJR2LxSUjmNSDvdsnXFLlypnOz2c6LYOOYn2jYOJb8rX4FfYhX10JbA4oNoHOQzZd0DrLDv2G9qJ/F7rOXHT8zLsrJsDLigfR4mWAUSuT/nm/shgLAHQAKVoIVWetxp+ajiFoPRcn/Zvq992TTb1tbmQzY22vfbIlqJp1OdgYPDZXftAPyQ/WePTLJMPQBunkz/xy9HoiJEbhy/hpObz+CQw0Oo0PjI+jY5DACPY/DTmfpnBAB5gmEe0iJc0KUt2vXzJOIu5s33c3ZOb+5U4sWlVNOrTGxICIqjI0D4FxfLpbITbcwCblTU6IvZkgXg7xM+WE246K691KRFJ1lH8zL8oG+NIlBwetZ0G5aNSGA7JsywTAkGoafhuSjuASylLUepj9lkyvFvhb8/JhJkDbc3eW38BER8rXIycCFI8dx6ehhZF09Atfcw2hY6wh83RMtul7KbTfEXgvDpbQw3MxrhUzHMNh4t4RvfS8EuAABdaCq1sVSQgBxcfnJw/79Cg4c8MG1ayXfWKeTtYtt28pamscfl80LrQkTCyKi8mDrIpu5uDYs+VghZFOrkjqnG5KQrOuyTwmUMnwwty/7N/V3fuoVW9y4lQZvH3/o7JzuOt9K/xtR7vSvcfAGarUt/Ji8LOD2v9CnxiHl6lG42yZDl3HxTgJyoVS1HoUqY60HkSpCD6SdN2vGpKSdQZDQI8gWQDHfx+TpdTh1pSkOX2yFIxfDcDi+FQ7Ht8KF64GQ9aNFs7OTwy0HBMilfn3Z1EsIWUNS8Kel2ww/8/LkfBIxMab9SWSZzL+scHSUTb3ats1fwsKgyczYVYmV/o9ARKQhRZEdue09ATQp+Xh9HgC97KOhRVsXvR552QmAayV8XViT2DgArsGAcxAyleZwv/vr1mJrPeIL1HoUMeiAqlqPO8lHKft6UA0mhHymclLMl4xLBeaEOFqKOSH8TJoxCY8wXElthpv/OkFcBDwuAkEBgE0DoPZF4OLF4kd2zcmRQzKfP18+b9lSHh56tG2roF07xZhEhIRUwzk+KgFDQkRU1elsUNg3ZlTNlaLWwyTZuLvZVUXVejg3kO3bWetRtQkhP+ibJAPJ+evZyXBJugzlsh7ITZVDmOakALmFJBBl7adl4wh4tLxrRKYw2T+sAAVAfW+gflDRl8rMlDPTX7xY9HLrVtmKaQl/f9NaiNat9XB2ToCfny90OibhJWFiQUREVFUZaj1cgwvfz1qP6kvo7yQEyYXXEtydIBS1Lzf1TlPJwukAuJVnuV2DC5kTonG59W1ydJRzYjQqZtTxtDQ5T8e//wK5ubIiUFHyfxZct3SbosgJJ31Nc6HymB/PqjCxICIiqq4sqvXIBm5fYq1HedHnyck8LUkAclPyawjMaglSUWTCV1lsnAE79yIWj/x1B285H4RHy0qdE6Iorq75o1RR1cLEgoiIqCazsbeg1uOW6VC6lVnr4dxAfnCt6FoPfZ78dl91DUFayfeqaLYu+R/6bd0Bew/T1wUSBL2tG5LSBTx9AqCz9yywz63Sh2ylmo+JBRERkTVTlPzJJout9fg3fzSryqz1cKwPJScHSMsA8kqoKTDUCBTWj8DSDscVydbVtCaguFqCovbZupZuNDa9HtkJCYAPB1+gisfEgoiIiIpnY1/8cMqGWo+CQ+mWU62HDoBfub2RslDkt/t31wgYaglsLUwQbF0rZ44VIg0xsSAiIiJ1CtZ6eLUp/BhjrUd8Ec2uiqv1KFOhSlcTUNQ+Wxc5ESQRlYiJBREREVW8UtV6GJKOCxBpccjOuAl7l9pQ7DxkTUGhtQR3JQm2Lhytiqo3nS1Q587U5l5FNFOsYphYEBERkfaKqPUQej1uJSTA19cXCvsIkDWxdQHu36x1KUqFf6FERERERKQaEwsiIiIiIlKNiQUREREREanGxIKIiIiIiFRjYkFERERERKoxsSAiIiIiItWYWBARERERkWpMLIiIiIiISDUmFkREREREpBoTCyIiIiIiUo2JBRERERERqcbEgoiIiIiIVGNiQUREREREqjGxICIiIiIi1ZhYEBERERGRarZaF6CyCSEAACkpKaU6T6/XIzU1FY6OjtDpmI+VFuOnDuOnDuOnDuOnDuOnDuOnDuOnDuOX/5nZ8Bm6OFaXWKSmpgIAAgICNC4JEREREVH1kJqaCg8Pj2KPUYQl6UcNotfrcfnyZbi5uUFRFIvPS0lJQUBAAC5evAh3d/cKLGHNxPipw/ipw/ipw/ipw/ipw/ipw/ipw/jJmorU1FT4+/uXWGtjdTUWOp0O9evXL/P57u7uVvtglQfGTx3GTx3GTx3GTx3GTx3GTx3GTx1rj19JNRUG1tlYjIiIiIiIyhUTCyIiIiIiUo2JhYUcHBwwdepUODg4aF2UaonxU4fxU4fxU4fxU4fxU4fxU4fxU4fxKx2r67xNRERERETljzUWRERERESkGhMLIiIiIiJSjYkFERERERGpxsTCAgsWLEBQUBAcHR3RuXNn7NmzR+siVQmzZs1Cx44d4ebmBl9fXzz00EOIjY01OaZnz55QFMVkee6550yOiY+Px4ABA+Ds7AxfX1+89tpryM3Nrcy3oolp06aZxSY0NNS4PzMzE+PHj4e3tzdcXV3x8MMP49q1aybXsNbYAUBQUJBZ/BRFwfjx4wHw2bvb33//jYEDB8Lf3x+KomDdunUm+4UQmDJlCurWrQsnJydERETg9OnTJsfcvHkTI0eOhLu7Ozw9PfHUU08hLS3N5JjDhw/j3nvvhaOjIwICAvDxxx9X9FurFMXFLycnB2+88QbCwsLg4uICf39/jBo1CpcvXza5RmHP7IcffmhyjDXGDwDGjBljFpu+ffuaHMPnr+j4FfZvoaIomD17tvEYa37+LPm8Ul7/527btg3t2rWDg4MDGjdujOXLl1f026taBBVr1apVwt7eXixdulQcO3ZMjBs3Tnh6eopr165pXTTN9enTRyxbtkwcPXpUxMTEiP79+4sGDRqItLQ04zE9evQQ48aNE1euXDEuycnJxv25ubmiZcuWIiIiQhw8eFCsX79e1K5dW0yePFmLt1Sppk6dKlq0aGESm8TEROP+5557TgQEBIioqCixb98+0aVLF9G1a1fjfmuOnRBCJCQkmMRu8+bNAoDYunWrEILP3t3Wr18v3n77bbFmzRoBQKxdu9Zk/4cffig8PDzEunXrxKFDh8SgQYNEw4YNxe3bt43H9O3bV7Ru3Vrs2rVLbN++XTRu3Fg89thjxv3JycnCz89PjBw5Uhw9elT88MMPwsnJSXz11VeV9TYrTHHxS0pKEhEREWL16tXi5MmTIjo6WnTq1Em0b9/e5BqBgYFixowZJs9kwX8vrTV+QggxevRo0bdvX5PY3Lx50+QYPn9Fx69g3K5cuSKWLl0qFEURZ8+eNR5jzc+fJZ9XyuP/3HPnzglnZ2cRGRkpjh8/LubPny9sbGzEhg0bKvX9aomJRQk6deokxo8fb3ydl5cn/P39xaxZszQsVdWUkJAgAIi//vrLuK1Hjx7ixRdfLPKc9evXC51OJ65evWrc9sUXXwh3d3eRlZVVkcXV3NSpU0Xr1q0L3ZeUlCTs7OzETz/9ZNx24sQJAUBER0cLIaw7doV58cUXRaNGjYRerxdC8Nkrzt0fTPR6vahTp46YPXu2cVtSUpJwcHAQP/zwgxBCiOPHjwsAYu/evcZj/vjjD6Eoivj333+FEEIsXLhQeHl5mcTvjTfeECEhIRX8jipXYR/s7rZnzx4BQFy4cMG4LTAwUHzyySdFnmPN8Rs9erQYPHhwkefw+ctnyfM3ePBgcf/995ts4/OX7+7PK+X1f+7rr78uWrRoYXKvESNGiD59+lT0W6oy2BSqGNnZ2di/fz8iIiKM23Q6HSIiIhAdHa1hyaqm5ORkAECtWrVMtn///feoXbs2WrZsicmTJyMjI8O4Lzo6GmFhYfDz8zNu69OnD1JSUnDs2LHKKbiGTp8+DX9/fwQHB2PkyJGIj48HAOzfvx85OTkmz15oaCgaNGhgfPasPXYFZWdn47vvvsOTTz4JRVGM2/nsWeb8+fO4evWqyfPm4eGBzp07mzxvnp6e6NChg/GYiIgI6HQ67N6923hM9+7dYW9vbzymT58+iI2Nxa1btyrp3VQNycnJUBQFnp6eJts//PBDeHt7o23btpg9e7ZJMwprj9+2bdvg6+uLkJAQPP/887hx44ZxH58/y127dg2///47nnrqKbN9fP6kuz+vlNf/udHR0SbXMBxjTZ8ZbbUuQFV2/fp15OXlmTxEAODn54eTJ09qVKqqSa/X46WXXkK3bt3QsmVL4/bHH38cgYGB8Pf3x+HDh/HGG28gNjYWa9asAQBcvXq10Pga9tVknTt3xvLlyxESEoIrV65g+vTpuPfee3H06FFcvXoV9vb2Zh9K/Pz8jHGx5tjdbd26dUhKSsKYMWOM2/jsWc7wfguLR8HnzdfX12S/ra0tatWqZXJMw4YNza5h2Ofl5VUh5a9qMjMz8cYbb+Cxxx6Du7u7cfukSZPQrl071KpVCzt37sTkyZNx5coVzJ07F4B1x69v374YOnQoGjZsiLNnz+Ktt95Cv379EB0dDRsbGz5/pfDNN9/Azc0NQ4cONdnO508q7PNKef2fW9QxKSkpuH37NpycnCriLVUpTCyoXIwfPx5Hjx7FP//8Y7L9mWeeMa6HhYWhbt26eOCBB3D27Fk0atSosotZpfTr18+43qpVK3Tu3BmBgYH48ccfreIfn/K0ZMkS9OvXD/7+/sZtfPZICzk5ORg+fDiEEPjiiy9M9kVGRhrXW7VqBXt7ezz77LOYNWuW1c/q++ijjxrXw8LC0KpVKzRq1Ajbtm3DAw88oGHJqp+lS5di5MiRcHR0NNnO508q6vMKlQ82hSpG7dq1YWNjYzYqwLVr11CnTh2NSlX1TJgwAb/99hu2bt2K+vXrF3ts586dAQBnzpwBANSpU6fQ+Br2WRNPT080bdoUZ86cQZ06dZCdnY2kpCSTYwo+e4yddOHCBWzZsgVPP/10scfx2Sua4f0W929dnTp1kJCQYLI/NzcXN2/e5DN5hyGpuHDhAjZv3mxSW1GYzp07Izc3F3FxcQAYv4KCg4NRu3Ztk79XPn8l2759O2JjY0v89xCwzuevqM8r5fV/blHHuLu7W80XhkwsimFvb4/27dsjKirKuE2v1yMqKgrh4eEalqxqEEJgwoQJWLt2Lf7880+zKtTCxMTEAADq1q0LAAgPD8eRI0dM/sMw/IfcvHnzCil3VZWWloazZ8+ibt26aN++Pezs7EyevdjYWMTHxxufPcZOWrZsGXx9fTFgwIBij+OzV7SGDRuiTp06Js9bSkoKdu/ebfK8JSUlYf/+/cZj/vzzT+j1emPSFh4ejr///hs5OTnGYzZv3oyQkJAa04yiKIak4vTp09iyZQu8vb1LPCcmJgY6nc7YxMea43e3S5cu4caNGyZ/r3z+SrZkyRK0b98erVu3LvFYa3r+Svq8Ul7/54aHh5tcw3CMVX1m1LjzeJW3atUq4eDgIJYvXy6OHz8unnnmGeHp6WkyKoC1ev7554WHh4fYtm2byfB1GRkZQgghzpw5I2bMmCH27dsnzp8/L3799VcRHBwsunfvbryGYfi23r17i5iYGLFhwwbh4+NTY4f8LOiVV14R27ZtE+fPnxc7duwQERERonbt2iIhIUEIIYe+a9Cggfjzzz/Fvn37RHh4uAgPDzeeb82xM8jLyxMNGjQQb7zxhsl2PnvmUlNTxcGDB8XBgwcFADF37lxx8OBB46hFH374ofD09BS//vqrOHz4sBg8eHChw822bdtW7N69W/zzzz+iSZMmJsN9JiUlCT8/P/HEE0+Io0ePilWrVglnZ+caMVxlcfHLzs4WgwYNEvXr1xcxMTEm/x4aRovZuXOn+OSTT0RMTIw4e/as+O6774SPj48YNWqU8R7WGr/U1FTx6quviujoaHH+/HmxZcsW0a5dO9GkSRORmZlpvAafv6L/foWQw8U6OzuLL774wux8a3/+Svq8IkT5/J9rGG72tddeEydOnBALFizgcLNkbv78+aJBgwbC3t5edOrUSezatUvrIlUJAApdli1bJoQQIj4+XnTv3l3UqlVLODg4iMaNG4vXXnvNZC4BIYSIi4sT/fr1E05OTqJ27drilVdeETk5ORq8o8o1YsQIUbduXWFvby/q1asnRowYIc6cOWPcf/v2bfHCCy8ILy8v4ezsLIYMGSKuXLlicg1rjZ3Bxo0bBQARGxtrsp3PnrmtW7cW+vc6evRoIYQccvbdd98Vfn5+wsHBQTzwwANmcb1x44Z47LHHhKurq3B3dxdjx44VqampJsccOnRI3HPPPcLBwUHUq1dPfPjhh5X1FitUcfE7f/58kf8eGuZV2b9/v+jcubPw8PAQjo6OolmzZmLmzJkmH5yFsM74ZWRkiN69ewsfHx9hZ2cnAgMDxbhx48y+wOPzV/TfrxBCfPXVV8LJyUkkJSWZnW/tz19Jn1eEKL//c7du3SratGkj7O3tRXBwsMk9rIEihBAVVBlCRERERERWgn0siIiIiIhINSYWRERERESkGhMLIiIiIiJSjYkFERERERGpxsSCiIiIiIhUY2JBRERERESqMbEgIiIiIiLVmFgQEREREZFqTCyIiKhQiqJg3bp1Fh8/ZswYPPTQQ6ruGRcXB0VREBMTo+o6RERU+ZhYEBFZmatXr+LFF19E48aN4ejoCD8/P3Tr1g1ffPEFMjIytC5eic6fP4/HH38c/v7+cHR0RP369TF48GCcPHkSAJMTIiKt2GpdACIiqjznzp1Dt27d4OnpiZkzZyIsLAwODg44cuQIFi1ahHr16mHQoEFaF7NIOTk56NWrF0JCQrBmzRrUrVsXly5dwh9//IGkpCSti0dEZNVYY0FEZEVeeOEF2NraYt++fRg+fDiaNWuG4OBgDB48GL///jsGDhxY5LlHjhzB/fffDycnJ3h7e+OZZ55BWlqa2XHTp0+Hj48P3N3d8dxzzyE7O9u4b8OGDbjnnnvg6ekJb29vPPjggzh79qzF5T927BjOnj2LhQsXokuXLggMDES3bt3w/vvvo0uXLgCAhg0bAgDatm0LRVHQs2dP4/lff/01mjVrBkdHR4SGhmLhwoXGfYaajlWrVqFr165wdHREy5Yt8ddff1lcPiIia8bEgojISty4cQObNm3C+PHj4eLiUugxiqIUuj09PR19+vSBl5cX9u7di59++glbtmzBhAkTTI6LiorCiRMnsG3bNvzwww9Ys2YNpk+fbnKdyMhI7Nu3D1FRUdDpdBgyZAj0er1F78HHxwc6nQ4///wz8vLyCj1mz549AIAtW7bgypUrWLNmDQDg+++/x5QpU/DBBx/gxIkTmDlzJt5991188803Jue/9tpreOWVV3Dw4EGEh4dj4MCBuHHjhkXlIyKyaoKIiKzCrl27BACxZs0ak+3e3t7CxcVFuLi4iNdff924HYBYu3atEEKIRYsWCS8vL5GWlmbc//vvvwudTieuXr0qhBBi9OjRolatWiI9Pd14zBdffCFcXV1FXl5eoWVKTEwUAMSRI0eEEEKcP39eABAHDx4s8n18/vnnwtnZWbi5uYn77rtPzJgxQ5w9e9a4v6hrNGrUSKxcudJk23vvvSfCw8NNzvvwww+N+3NyckT9+vXFRx99VGR5iIhIYo0FEZGV27NnD2JiYtCiRQtkZWUVesyJEyfQunVrk5qObt26Qa/XIzY21ritdevWcHZ2Nr4ODw9HWloaLl68CAA4ffo0HnvsMQQHB8Pd3R1BQUEAgPj4eIvLO378eFy9ehXff/89wsPD8dNPP6FFixbYvHlzkeekp6fj7NmzeOqpp+Dq6mpc3n//fbOmWOHh4cZ1W1tbdOjQASdOnLC4fERE1oqdt4mIrETjxo2hKIpJIgAAwcHBAAAnJ6cKL8PAgQMRGBiIxYsXw9/fH3q9Hi1btjTph2EJNzc3DBw4EAMHDsT777+PPn364P3330evXr0KPd7QF2Tx4sXo3LmzyT4bG5uyvRkiIjLBGgsiIivh7e2NXr164fPPP0d6enqpzm3WrBkOHTpkct6OHTug0+kQEhJi3Hbo0CHcvn3b+HrXrl1wdXVFQEAAbty4gdjYWLzzzjt44IEH0KxZM9y6dUv1+1IUBaGhocay2dvbA4BJHww/Pz/4+/vj3LlzaNy4scli6OxdsMwGubm52L9/P5o1a6a6nERENR0TCyIiK7Jw4ULk5uaiQ4cOWL16NU6cOIHY2Fh89913OHnyZJHf3o8cORKOjo4YPXo0jh49iq1bt2LixIl44okn4OfnZzwuOzsbTz31FI4fP47169dj6tSpmDBhAnQ6Hby8vODt7Y1FixbhzJkz+PPPPxEZGVmq8sfExGDw4MH4+eefcfz4cZw5cwZLlizB0qVLMXjwYACAr68vnJycsGHDBly7dg3JyckA5GhVs2bNwmeffYZTp07hyJEjWLZsGebOnWtyjwULFmDt2rU4efIkxo8fj1u3buHJJ58sVTmJiKyS1p08iIiocl2+fFlMmDBBNGzYUNjZ2QlXV1fRqVMnMXv2bJOO1yjQeVsIIQ4fPizuu+8+4ejoKGrVqiXGjRsnUlNTjftHjx4tBg8eLKZMmSK8vb2Fq6urGDdunMjMzDQes3nzZtGsWTPh4OAgWrVqJbZt22Zyn5I6bycmJopJkyaJli1bCldXV+Hm5ibCwsLEnDlzTDqIL168WAQEBAidTid69Ohh3P7999+LNm3aCHt7e+Hl5SW6d+9u7MxuuPfKlStFp06dhL29vWjevLn4888/VUSbiMh6KEIIoW1qQ0REpL24uDg0bNgQBw8eRJs2bbQuDhFRtcOmUEREREREpBoTCyIiIiIiUo1NoYiIiIiISDXWWBARERERkWpMLIiIiIiISDUmFkREREREpBoTCyIiIiIiUo2JBRERERERqcbEgoiIiIiIVGNiQUREREREqjGxICIiIiIi1ZhYEBERERGRav8PwnEimCrB43cAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# ============================================\n",
        "# 0) INSTALL DEPENDENCIES\n",
        "# ============================================\n",
        "!pip install -q transformers datasets accelerate evaluate tensorboard scipy nltk\n",
        "\n",
        "# ============================================\n",
        "# 1) IMPORTS\n",
        "# ============================================\n",
        "import re\n",
        "import json\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "from scipy.signal import savgol_filter\n",
        "\n",
        "import nltk\n",
        "from nltk.corpus import stopwords\n",
        "\n",
        "from datasets import Dataset\n",
        "from transformers import (\n",
        "    AutoTokenizer,\n",
        "    AutoModelForSequenceClassification,\n",
        "    Trainer,\n",
        "    TrainingArguments,\n",
        "    EarlyStoppingCallback,\n",
        "    TrainerCallback,\n",
        ")\n",
        "import evaluate\n",
        "\n",
        "# ============================================\n",
        "# 2) LOAD + CLEAN DATA\n",
        "# ============================================\n",
        "nltk.download(\"stopwords\")\n",
        "stop_words = set(stopwords.words(\"english\"))\n",
        "\n",
        "df = pd.read_csv(path + '/mobile_legends_reviews.csv')\n",
        "df[\"content\"] = df[\"content\"].astype(str).str.lower()\n",
        "\n",
        "def remove_emoji(text):\n",
        "    emoji_pattern = re.compile(\n",
        "        \"[\" u\"\\U0001F600-\\U0001F64F\" u\"\\U0001F300-\\U0001F5FF\"\n",
        "          u\"\\U0001F680-\\U0001F6FF\" u\"\\U0001F1E0-\\U0001F1FF\" \"]+\",\n",
        "        flags=re.UNICODE)\n",
        "    return emoji_pattern.sub(r\"\", text)\n",
        "\n",
        "df[\"content\"] = df[\"content\"].apply(remove_emoji)\n",
        "df[\"content\"] = df[\"content\"].str.replace(r\"[^a-zA-Z0-9\\s.,!?;:]\", \"\", regex=True)\n",
        "df[\"content\"] = df[\"content\"].str.replace(r\"\\s+\", \" \", regex=True).str.strip()\n",
        "\n",
        "def remove_stopwords(text):\n",
        "    return \" \".join([w for w in text.split() if w not in stop_words])\n",
        "\n",
        "df[\"cleaned\"] = df[\"content\"].apply(remove_stopwords)\n",
        "\n",
        "# sentiment labels\n",
        "df[\"sentiment\"] = df[\"score\"].apply(lambda r: \"negative\" if r <= 3 else \"positive\")\n",
        "\n",
        "df = df.drop_duplicates(subset=[\"content\"])\n",
        "df = df[[\"cleaned\", \"sentiment\"]].reset_index(drop=True)\n",
        "\n",
        "# ============================================\n",
        "# 3) HUGGINGFACE DATASET + TOKENIZATION\n",
        "# ============================================\n",
        "dataset = Dataset.from_pandas(df)\n",
        "dataset = dataset.class_encode_column(\"sentiment\")\n",
        "dataset = dataset.train_test_split(test_size=0.2, seed=42)\n",
        "\n",
        "model_name = \"roberta-base\"   # stable + accurate\n",
        "tokenizer = AutoTokenizer.from_pretrained(model_name)\n",
        "\n",
        "def tokenize(batch):\n",
        "    return tokenizer(\n",
        "        batch[\"cleaned\"],\n",
        "        truncation=True,\n",
        "        padding=\"max_length\",\n",
        "        max_length=256,\n",
        "    )\n",
        "\n",
        "tokenized = dataset.map(tokenize, batched=True)\n",
        "tokenized = tokenized.remove_columns([\"cleaned\"])\n",
        "tokenized = tokenized.rename_column(\"sentiment\", \"labels\")\n",
        "tokenized.set_format(\"torch\")\n",
        "\n",
        "# ============================================\n",
        "# 4) MODEL CONFIG (Anti-Overfitting)\n",
        "# ============================================\n",
        "num_labels = len(tokenized[\"train\"].features[\"labels\"].names)\n",
        "model = AutoModelForSequenceClassification.from_pretrained(\n",
        "    model_name,\n",
        "    num_labels=num_labels\n",
        ")\n",
        "\n",
        "# Strong anti-overfitting\n",
        "model.config.hidden_dropout_prob = 0.25\n",
        "model.config.attention_probs_dropout_prob = 0.25\n",
        "model.config.label_smoothing_factor = 0.12\n",
        "model.config.gradient_checkpointing = True\n",
        "\n",
        "accuracy_metric = evaluate.load(\"accuracy\")\n",
        "\n",
        "def compute_metrics(pred):\n",
        "    logits, labels = pred\n",
        "    preds = np.argmax(logits, axis=-1)\n",
        "    return accuracy_metric.compute(predictions=preds, references=labels)\n",
        "\n",
        "# ============================================\n",
        "# 5) METRICS CALLBACK (for smoothed curves)\n",
        "# ============================================\n",
        "class MetricsCallback(TrainerCallback):\n",
        "    def __init__(self):\n",
        "        self.train_steps = []\n",
        "        self.train_loss = []\n",
        "        self.eval_steps = []\n",
        "        self.val_loss = []\n",
        "        self.val_acc = []\n",
        "\n",
        "    def on_log(self, args, state, control, logs=None, **kwargs):\n",
        "        if logs is None:\n",
        "            return\n",
        "        if \"loss\" in logs:\n",
        "            self.train_steps.append(state.global_step)\n",
        "            self.train_loss.append(logs[\"loss\"])\n",
        "        if \"eval_loss\" in logs:\n",
        "            self.eval_steps.append(state.global_step)\n",
        "            self.val_loss.append(logs[\"eval_loss\"])\n",
        "        if \"eval_accuracy\" in logs:\n",
        "            self.val_acc.append(logs[\"eval_accuracy\"])\n",
        "\n",
        "metrics_callback = MetricsCallback()\n",
        "\n",
        "# ============================================\n",
        "# 6) TRAINING ARGUMENTS (improved)\n",
        "# ============================================\n",
        "training_args = TrainingArguments(\n",
        "    output_dir=\"sentiment_roberta_enhanced\",\n",
        "    eval_strategy=\"steps\",\n",
        "    eval_steps=300,\n",
        "    save_strategy=\"steps\",\n",
        "    save_steps=300,\n",
        "    learning_rate=8e-6,\n",
        "    warmup_ratio=0.1,\n",
        "    lr_scheduler_type=\"cosine\",\n",
        "    per_device_train_batch_size=16,\n",
        "    per_device_eval_batch_size=16,\n",
        "    gradient_accumulation_steps=2,\n",
        "    num_train_epochs=8,\n",
        "    weight_decay=0.15,\n",
        "    logging_steps=60,\n",
        "    report_to=\"none\",\n",
        "    fp16=True,\n",
        "    load_best_model_at_end=True,\n",
        "    metric_for_best_model=\"eval_accuracy\",\n",
        ")\n",
        "\n",
        "trainer = Trainer(\n",
        "    model=model,\n",
        "    args=training_args,\n",
        "    train_dataset=tokenized[\"train\"],\n",
        "    eval_dataset=tokenized[\"test\"],\n",
        "    compute_metrics=compute_metrics,\n",
        "    callbacks=[\n",
        "        metrics_callback,\n",
        "        EarlyStoppingCallback(early_stopping_patience=2)\n",
        "    ],\n",
        ")\n",
        "\n",
        "# ============================================\n",
        "# 7) TRAIN + EVALUATE\n",
        "# ============================================\n",
        "trainer.train()\n",
        "\n",
        "results = trainer.evaluate()\n",
        "print(\"🔥 FINAL ACCURACY:\", results[\"eval_accuracy\"])\n",
        "\n",
        "# ============================================\n",
        "# 8) PLOTTING (smooth & connected curves)\n",
        "# ============================================\n",
        "\n",
        "train_steps = np.array(metrics_callback.train_steps)\n",
        "train_loss = np.array(metrics_callback.train_loss)\n",
        "eval_steps = np.array(metrics_callback.eval_steps)\n",
        "val_loss = np.array(metrics_callback.val_loss)\n",
        "val_acc = np.array(metrics_callback.val_acc)\n",
        "\n",
        "# ----- Smooth training loss -----\n",
        "if len(train_loss) >= 7:\n",
        "    train_loss_smooth = savgol_filter(train_loss, 7, 3)\n",
        "else:\n",
        "    train_loss_smooth = train_loss\n",
        "\n",
        "# -------- ACCURACY CURVE --------\n",
        "plt.figure(figsize=(8,5))\n",
        "plt.plot(eval_steps, val_acc, color=\"orange\", linewidth=2, label=\"Validation Accuracy\")\n",
        "plt.title(\"Validation Accuracy (RoBERTa)\")\n",
        "plt.xlabel(\"Global Step\")\n",
        "plt.ylabel(\"Accuracy\")\n",
        "plt.grid(True, alpha=0.3)\n",
        "plt.legend()\n",
        "plt.tight_layout()\n",
        "plt.show()\n",
        "\n",
        "# -------- LOSS CURVE --------\n",
        "plt.figure(figsize=(8,5))\n",
        "plt.plot(train_steps, train_loss_smooth, color=\"blue\", linewidth=2, label=\"Train Loss\")\n",
        "plt.plot(eval_steps, val_loss, color=\"orange\", linewidth=2, label=\"Validation Loss\")\n",
        "plt.title(\"Train vs Validation Loss (Smoothed, RoBERTa)\")\n",
        "plt.xlabel(\"Global Step\")\n",
        "plt.ylabel(\"Loss\")\n",
        "plt.grid(True, alpha=0.3)\n",
        "plt.legend()\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import classification_report, accuracy_score\n",
        "import numpy as np\n",
        "\n",
        "# Get true labels from the tokenized test dataset\n",
        "y_true_labels = tokenized[\"test\"][\"labels\"]\n",
        "\n",
        "# Get predictions from the trained model\n",
        "predictions = trainer.predict(tokenized[\"test\"])\n",
        "\n",
        "# Convert logits to predicted labels\n",
        "y_pred_labels = np.argmax(predictions.predictions, axis=-1)\n",
        "\n",
        "# Get the mapping from integer labels back to sentiment names\n",
        "label_names = tokenized[\"test\"].features[\"labels\"].names\n",
        "\n",
        "print(\"Accuracy:\", accuracy_score(y_true_labels, y_pred_labels))\n",
        "print(\"\\nClassification Report:\")\n",
        "print(classification_report(y_true_labels, y_pred_labels, digits=4, target_names=label_names))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 231
        },
        "id": "DB5fSvS5hcLV",
        "outputId": "76ab8dd4-f1f9-4b45-aec9-66e01bff6534"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": []
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 0.9037932930181418\n",
            "\n",
            "Classification Report:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "    negative     0.9050    0.9120    0.9085      4764\n",
            "    positive     0.9024    0.8947    0.8986      4331\n",
            "\n",
            "    accuracy                         0.9038      9095\n",
            "   macro avg     0.9037    0.9034    0.9035      9095\n",
            "weighted avg     0.9038    0.9038    0.9038      9095\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(8, 5))\n",
        "plt.plot(train_steps, train_loss_smooth, linewidth=2, label=\"Train Loss\")\n",
        "plt.plot(eval_steps, val_loss, linewidth=2, label=\"Validation Loss\")\n",
        "plt.title(\"Training and Validation Loss (RoBERTa)\")\n",
        "plt.xlabel(\"Global Step\")\n",
        "plt.ylabel(\"Loss\")\n",
        "plt.grid(True, alpha=0.2)\n",
        "plt.legend()\n",
        "plt.tight_layout()\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 507
        },
        "id": "RIAPndsIBG9C",
        "outputId": "b31dc52d-1703-4920-bd28-145c3f690698"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 997
        },
        "id": "T2zHvIlNArkp",
        "outputId": "fb5fe430-b9e6-4824-b346-97c5fda1a5dd"
      },
      "source": [
        "# Re-extract metrics from trainer.state.log_history for consistent lengths\n",
        "log_history = trainer.state.log_history\n",
        "\n",
        "eval_steps_filtered = []\n",
        "val_acc_filtered = []\n",
        "val_loss_filtered = []\n",
        "\n",
        "for entry in log_history:\n",
        "    # Only consider entries that contain both eval_accuracy and eval_loss for synchronization\n",
        "    if \"eval_accuracy\" in entry and \"eval_loss\" in entry:\n",
        "        eval_steps_filtered.append(entry[\"step\"])\n",
        "        val_acc_filtered.append(entry[\"eval_accuracy\"])\n",
        "        val_loss_filtered.append(entry[\"eval_loss\"])\n",
        "\n",
        "# Convert to numpy arrays\n",
        "eval_steps = np.array(eval_steps_filtered)\n",
        "val_acc = np.array(val_acc_filtered)\n",
        "val_loss = np.array(val_loss_filtered)\n",
        "\n",
        "# Ensure train_steps and train_loss are also numpy arrays for consistent plotting\n",
        "train_steps = np.array(metrics_callback.train_steps)\n",
        "train_loss = np.array(metrics_callback.train_loss)\n",
        "\n",
        "# Smooth training loss (if applicable, using the corrected train_loss)\n",
        "if len(train_loss) >= 7:\n",
        "    train_loss_smooth = savgol_filter(train_loss, 7, 3)\n",
        "else:\n",
        "    train_loss_smooth = train_loss\n",
        "\n",
        "# -------- ACCURACY CURVE --------\n",
        "plt.figure(figsize=(8,5))\n",
        "plt.plot(eval_steps, val_acc, color=\"orange\", linewidth=2, label=\"Validation Accuracy\")\n",
        "plt.title(\"Validation Accuracy (RoBERTa)\")\n",
        "plt.xlabel(\"Global Step\")\n",
        "plt.ylabel(\"Accuracy\")\n",
        "plt.grid(True, alpha=0.3)\n",
        "plt.legend()\n",
        "plt.tight_layout()\n",
        "plt.show()\n",
        "\n",
        "# -------- LOSS CURVE --------\n",
        "plt.figure(figsize=(8,5))\n",
        "plt.plot(train_steps, train_loss_smooth, color=\"blue\", linewidth=2, label=\"Train Loss\")\n",
        "plt.plot(eval_steps, val_loss, color=\"orange\", linewidth=2, label=\"Validation Loss\")\n",
        "plt.title(\"Train vs Validation Loss (RoBERTa)\")\n",
        "plt.xlabel(\"Global Step\")\n",
        "plt.ylabel(\"Loss\")\n",
        "plt.grid(True, alpha=0.3)\n",
        "plt.legend()\n",
        "plt.tight_layout()\n",
        "plt.show()\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "# -------- ACCURACY CURVE --------\n",
        "plt.figure(figsize=(8,5))\n",
        "plt.plot(eval_steps, val_acc, color=\"orange\", linewidth=2, label=\"Validation Accuracy\")\n",
        "plt.title(\"Validation Accuracy (RoBERTa)\")\n",
        "plt.xlabel(\"Global Step\")\n",
        "plt.ylabel(\"Accuracy\")\n",
        "plt.grid(True, alpha=0.3)\n",
        "plt.legend()\n",
        "plt.tight_layout()\n",
        "plt.show()\n",
        "\n",
        "\n",
        "train_loss_on_eval = []\n",
        "j = 0\n",
        "last = train_loss[0]\n",
        "\n",
        "for s in eval_steps:\n",
        "    while j < len(train_steps) and train_steps[j] <= s:\n",
        "        last = train_loss[j]\n",
        "        j += 1\n",
        "    train_loss_on_eval.append(last)\n",
        "\n",
        "plt.figure(figsize=(8, 5))\n",
        "plt.plot(eval_steps, train_loss_on_eval, linewidth=2, label=\"Train Loss (Filled)\")\n",
        "plt.plot(eval_steps, val_loss, linewidth=2, label=\"Validation Loss\")\n",
        "plt.title(\"Training and Validation Loss (RoBERTa)\")\n",
        "plt.xlabel(\"Global Step\")\n",
        "plt.ylabel(\"Loss\")\n",
        "plt.grid(True, alpha=0.3)\n",
        "plt.legend()\n",
        "plt.tight_layout()\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 997
        },
        "id": "G0pgVaRDCkSy",
        "outputId": "b71867f1-101a-433c-aca9-f21691f96dfa"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zq4pkYEhgxHc"
      },
      "source": [
        "## 4- BERT-base-uncased"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
          "referenced_widgets": [
            "08eafde6dc3f4eef806d5014d1acc1a2",
            "c34e857b1db14ab3bc0a30c7e317c85a",
            "b2eb9698553d418297c40aa55fe10222",
            "41d1c1bd833b40c1b0778d01084cfeed",
            "4046448853d24f638a46f51476de7dfb",
            "43ac054f9a3041448b857251cf6691e9",
            "e8d04d1f3e0243edb74019820f0f280e",
            "062501abd90743a6889635ab01abc721",
            "ea4a0dfdc3c942c4a0aec307f1fa924d",
            "50d89be867294ad0abf0001d440172ed",
            "7f18b1a310d544ddb1f46dac1031dc5d",
            "cb3e0b1839424e949064cf168d46651d",
            "83f984404459455ca8a31ca7f536754c",
            "757d91dda52e4250ac3d6104faf20afe",
            "8f2b5702b2ea45569003ba6cfd1a37ee",
            "40a23128918d4391a7a6d41b2298cf13",
            "24a9019dfef640b681443eddc28315bf",
            "75c299f97ecd48059e8b0f8e32b8438f",
            "93bab72c35784163b66ff8ada5eb96b2",
            "032f93043d334bc2aeea6d4983754ace",
            "d7a581afce2e47efb0b3ff3497224946",
            "dce4d644b81349e8862ce182ecce8deb",
            "ddd378c56a7449e98e170aa6a5c5b7e4",
            "0d921365a5354d81895b1ee87a5169fc",
            "31045e3fc88e41a7b1900d9976b0d151",
            "03e768277e154f60a1266a970a075201",
            "7da372f3e6614b21a7c98142f14943bf",
            "2ac1fc3d502346be982440f0c7074611",
            "d52ff022fd2c47f889b25e89aaaab502",
            "679e333ef04f40c3ba6bf2f171c1cc01",
            "f535b6a2eefd480e9ad43980d36c992f",
            "b590f830b6a94242a9816f4d9db0bdd9",
            "9d046b1218604c32b931f6f7f038e7fe"
          ]
        },
        "id": "ax1UBcgtg5y_",
        "outputId": "e6bdfa73-b080-412a-ed67-24e7012fea8c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Package stopwords is already up-to-date!\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Casting to class labels:   0%|          | 0/45473 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "08eafde6dc3f4eef806d5014d1acc1a2"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/36378 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "cb3e0b1839424e949064cf168d46651d"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/9095 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "ddd378c56a7449e98e170aa6a5c5b7e4"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of RobertaForSequenceClassification were not initialized from the model checkpoint at roberta-base and are newly initialized: ['classifier.dense.bias', 'classifier.dense.weight', 'classifier.out_proj.bias', 'classifier.out_proj.weight']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='2100' max='9096' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [2100/9096 09:32 < 31:47, 3.67 it/s, Epoch 1/8]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Step</th>\n",
              "      <th>Training Loss</th>\n",
              "      <th>Validation Loss</th>\n",
              "      <th>Accuracy</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>300</td>\n",
              "      <td>0.415900</td>\n",
              "      <td>0.331087</td>\n",
              "      <td>0.869379</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>600</td>\n",
              "      <td>0.335600</td>\n",
              "      <td>0.285959</td>\n",
              "      <td>0.895217</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>900</td>\n",
              "      <td>0.323200</td>\n",
              "      <td>0.277311</td>\n",
              "      <td>0.895327</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1200</td>\n",
              "      <td>0.288400</td>\n",
              "      <td>0.272493</td>\n",
              "      <td>0.900495</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1500</td>\n",
              "      <td>0.289600</td>\n",
              "      <td>0.264046</td>\n",
              "      <td>0.903683</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1800</td>\n",
              "      <td>0.292400</td>\n",
              "      <td>0.267218</td>\n",
              "      <td>0.901814</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2100</td>\n",
              "      <td>0.278700</td>\n",
              "      <td>0.294574</td>\n",
              "      <td>0.898626</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='569' max='569' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [569/569 00:14]\n",
              "    </div>\n",
              "    "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "🔥 FINAL ACCURACY: 0.9036833424958769\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# ============================================\n",
        "# 0) INSTALL DEPENDENCIES\n",
        "# ============================================\n",
        "!pip install -q transformers datasets accelerate evaluate tensorboard scipy nltk\n",
        "\n",
        "# ============================================\n",
        "# 1) IMPORTS\n",
        "# ============================================\n",
        "import re\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "from scipy.signal import savgol_filter\n",
        "\n",
        "import nltk\n",
        "from nltk.corpus import stopwords\n",
        "\n",
        "from datasets import Dataset\n",
        "from transformers import (\n",
        "    AutoTokenizer,\n",
        "    AutoModelForSequenceClassification,\n",
        "    TrainingArguments,\n",
        "    Trainer,\n",
        "    EarlyStoppingCallback,\n",
        ")\n",
        "import evaluate\n",
        "\n",
        "# ============================================K\n",
        "# 2) LOAD + CLEAN DATA\n",
        "# ============================================\n",
        "nltk.download(\"stopwords\")\n",
        "stop_words = set(stopwords.words(\"english\"))\n",
        "\n",
        "df = pd.read_csv(path + '/mobile_legends_reviews.csv')\n",
        "df[\"content\"] = df[\"content\"].astype(str).str.lower()\n",
        "\n",
        "def remove_emoji(text):\n",
        "    emoji_pattern = re.compile(\n",
        "        \"[\" u\"\\U0001F600-\\U0001F64F\"\n",
        "          u\"\\U0001F300-\\U0001F5FF\"\n",
        "          u\"\\U0001F680-\\U0001F6FF\"\n",
        "          u\"\\U0001F1E0-\\U0001F1FF\" \"]+\",\n",
        "        flags=re.UNICODE)\n",
        "    return emoji_pattern.sub(\"\", text)\n",
        "\n",
        "df[\"content\"] = df[\"content\"].apply(remove_emoji)\n",
        "df[\"content\"] = df[\"content\"].str.replace(r\"[^a-zA-Z0-9\\s.,!?;:]\", \"\", regex=True)\n",
        "df[\"content\"] = df[\"content\"].str.replace(r\"\\s+\", \" \", regex=True).str.strip()\n",
        "\n",
        "def remove_stopwords(text):\n",
        "    return \" \".join([w for w in text.split() if w not in stop_words])\n",
        "\n",
        "df[\"cleaned\"] = df[\"content\"].apply(remove_stopwords)\n",
        "\n",
        "df[\"sentiment\"] = df[\"score\"].apply(lambda r: \"negative\" if r <= 3 else \"positive\")\n",
        "df = df.drop_duplicates(subset=[\"content\"])\n",
        "df = df[[\"cleaned\", \"sentiment\"]].reset_index(drop=True)\n",
        "\n",
        "# ============================================\n",
        "# 3) DATASET + TOKENIZER\n",
        "# ============================================\n",
        "dataset = Dataset.from_pandas(df)\n",
        "dataset = dataset.class_encode_column(\"sentiment\")\n",
        "dataset = dataset.train_test_split(0.2, seed=42)\n",
        "\n",
        "model_name = \"roberta-base\"\n",
        "tokenizer = AutoTokenizer.from_pretrained(model_name)\n",
        "\n",
        "def tokenize(batch):\n",
        "    return tokenizer(\n",
        "        batch[\"cleaned\"],\n",
        "        truncation=True,\n",
        "        padding=\"max_length\",\n",
        "        max_length=256,\n",
        "    )\n",
        "\n",
        "tokenized = dataset.map(tokenize, batched=True)\n",
        "tokenized = tokenized.remove_columns(\"cleaned\")\n",
        "tokenized = tokenized.rename_column(\"sentiment\", \"labels\")\n",
        "tokenized.set_format(\"torch\")\n",
        "\n",
        "# ============================================\n",
        "# 4) MODEL (Anti-Overfitting)\n",
        "# ============================================\n",
        "num_labels = len(tokenized[\"train\"].features[\"labels\"].names)\n",
        "model = AutoModelForSequenceClassification.from_pretrained(\n",
        "    model_name, num_labels=num_labels\n",
        ")\n",
        "\n",
        "model.config.hidden_dropout_prob = 0.25\n",
        "model.config.attention_probs_dropout_prob = 0.25\n",
        "model.config.label_smoothing_factor = 0.12\n",
        "\n",
        "metric = evaluate.load(\"accuracy\")\n",
        "\n",
        "def compute_metrics(pred):\n",
        "    logits, labels = pred\n",
        "    preds = np.argmax(logits, axis=-1)\n",
        "    return metric.compute(predictions=preds, references=labels)\n",
        "\n",
        "# ============================================\n",
        "# 5) TRAINING SETTINGS\n",
        "# ============================================\n",
        "training_args = TrainingArguments(\n",
        "    output_dir=\"sentiment_roberta\",\n",
        "    eval_strategy=\"steps\",\n",
        "    eval_steps=300,\n",
        "    save_strategy=\"steps\",\n",
        "    save_steps=300,\n",
        "    learning_rate=8e-6,\n",
        "    warmup_ratio=0.1,\n",
        "    lr_scheduler_type=\"cosine\",\n",
        "    per_device_train_batch_size=16,\n",
        "    per_device_eval_batch_size=16,\n",
        "    gradient_accumulation_steps=2,\n",
        "    num_train_epochs=8,\n",
        "    weight_decay=0.15,\n",
        "    logging_steps=60,\n",
        "    report_to=\"none\",\n",
        "    fp16=True,\n",
        "    load_best_model_at_end=True,\n",
        "    metric_for_best_model=\"eval_accuracy\",\n",
        ")\n",
        "\n",
        "trainer = Trainer(\n",
        "    model=model,\n",
        "    args=training_args,\n",
        "    train_dataset=tokenized[\"train\"],\n",
        "    eval_dataset=tokenized[\"test\"],\n",
        "    compute_metrics=compute_metrics,\n",
        "    callbacks=[EarlyStoppingCallback(early_stopping_patience=2)],\n",
        ")\n",
        "\n",
        "# ============================================\n",
        "# 6) TRAIN\n",
        "# ============================================\n",
        "trainer.train()\n",
        "results = trainer.evaluate()\n",
        "print(\"🔥 FINAL ACCURACY:\", results[\"eval_accuracy\"])\n",
        "\n",
        "# ============================================\n",
        "# 7) EXTRACT METRICS FOR PLOTTING\n",
        "# ============================================\n",
        "log = trainer.state.log_history\n",
        "\n",
        "train_steps, train_loss = [], []\n",
        "val_steps, val_loss = [], []\n",
        "val_epochs, val_acc = [], []\n",
        "\n",
        "for e in log:\n",
        "    if \"loss\" in e:\n",
        "        train_steps.append(e[\"step\"])\n",
        "        train_loss.append(e[\"loss\"])\n",
        "    if \"eval_loss\" in e:\n",
        "        val_steps.append(e[\"step\"])\n",
        "        val_loss.append(e[\"eval_loss\"])\n",
        "    if \"eval_accuracy\" in e:\n",
        "        val_epochs.append(e[\"epoch\"])\n",
        "        val_acc.append(e[\"eval_accuracy\"])\n",
        "\n",
        "train_steps = np.array(train_steps)\n",
        "train_loss  = np.array(train_loss)\n",
        "val_steps   = np.array(val_steps)\n",
        "val_loss    = np.array(val_loss)\n",
        "val_epochs  = np.array(val_epochs)\n",
        "val_acc     = np.array(val_acc)\n",
        "\n",
        "def smooth(values, window=7):\n",
        "    if len(values) < 5:\n",
        "        return values\n",
        "    if window % 2 == 0:\n",
        "        window -= 1\n",
        "    window = min(window, len(values))\n",
        "    return savgol_filter(values, window, 3)\n",
        "\n",
        "# ============================================\n",
        "# 8) SMOOTH ACCURACY CURVE (ORANGE)\n",
        "# ============================================\n",
        "plt.figure(figsize=(8,5))\n",
        "plt.plot(val_epochs, smooth(val_acc), color=\"orange\", linewidth=2, label=\"Validation Accuracy\")\n",
        "plt.title(\"Validation Accuracy (Smoothed)\")\n",
        "plt.xlabel(\"Epoch\")\n",
        "plt.ylabel(\"Accuracy\")\n",
        "plt.grid(True, alpha=0.3)\n",
        "plt.legend()\n",
        "plt.tight_layout()\n",
        "plt.show()\n",
        "\n",
        "# ============================================\n",
        "# 9) TRAIN vs VALIDATION LOSS (BLUE vs ORANGE)\n",
        "# ============================================\n",
        "plt.figure(figsize=(10,6))\n",
        "plt.plot(train_steps, smooth(train_loss, 21), color=\"blue\", linewidth=2, label=\"Train Loss\")\n",
        "plt.plot(val_steps, smooth(val_loss, 9), color=\"orange\", linewidth=2, label=\"Validation Loss\")\n",
        "plt.title(\"Train vs Validation Loss (Smoothed)\")\n",
        "plt.xlabel(\"Global Step\")\n",
        "plt.ylabel(\"Loss\")\n",
        "plt.grid(True, alpha=0.3)\n",
        "plt.legend()\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import classification_report, accuracy_score\n",
        "import numpy as np\n",
        "\n",
        "# Get true labels from the tokenized test dataset\n",
        "y_true_labels = tokenized[\"test\"][\"labels\"]\n",
        "\n",
        "# Get predictions from the trained model\n",
        "predictions = trainer.predict(tokenized[\"test\"])\n",
        "\n",
        "# Convert logits to predicted labels\n",
        "y_pred_labels = np.argmax(predictions.predictions, axis=-1)\n",
        "\n",
        "# Get the mapping from integer labels back to sentiment names\n",
        "label_names = tokenized[\"test\"].features[\"labels\"].names\n",
        "\n",
        "print(\"Accuracy:\", accuracy_score(y_true_labels, y_pred_labels))\n",
        "print(\"\\nClassification Report:\")\n",
        "print(classification_report(y_true_labels, y_pred_labels, digits=4, target_names=label_names))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 231
        },
        "id": "hkmjeiBmknFL",
        "outputId": "be0569f2-65a8-4750-c300-28e5f69855d5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": []
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 0.9036833424958769\n",
            "\n",
            "Classification Report:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "    negative     0.9055    0.9112    0.9083      4764\n",
            "    positive     0.9017    0.8954    0.8985      4331\n",
            "\n",
            "    accuracy                         0.9037      9095\n",
            "   macro avg     0.9036    0.9033    0.9034      9095\n",
            "weighted avg     0.9037    0.9037    0.9037      9095\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ca-1GdguchtM"
      },
      "source": [
        "## 5- Electra-Large-Discriminator"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
          "referenced_widgets": [
            "cbe77d2592eb461db0466f1bbdb3ad4b",
            "cba0037e9f164d4a9f0ef509850fdce0",
            "2ec4a44b6a094a6cb10a6bee3a289c98",
            "c6f276492f3c4e4ab106f13a566939ba",
            "9e76912b1c164755ad867c253d6806fe",
            "8d87d06dff414d9589ebc4927b079176",
            "19e429b687174717a5720d7c1d32f50f",
            "8796c296a98d4288ab3996fd4d5cd592",
            "d91b7e03b7514365a196ee4d32feca48",
            "c93e76da041845b089566e6f3a6f71a2",
            "307724486f644bf0807f765a01dd29a6",
            "06606f8a38554ce4b816b2a075a520b9",
            "968f980151a24dc3a27a8f1605e9cbf6",
            "c3d41aea65ef4959a1d61012e3e56ef3",
            "963fb742a14b433ca443df425b3f4363",
            "06f4da794b5d4690b151f3264b838125",
            "eb90a54ef98a4334910bd72dfef213f9",
            "c0963f1a2c494d82a1cec02e49fc59f6",
            "c12b685cccb44381adc88215bb407ba2",
            "a237ea52249240349a7bf48e65a53cab",
            "bb9af423cab84afe938cc040b7804e5f",
            "564271ffcd56441c93cf7b994413e075",
            "741cdad309a648149854fd4329fe0c9a",
            "1994c049a776410b8b3b80571084f68a",
            "0dca5a0b7bbf4258b5a49793941e2ed6",
            "fc6206bc78f8467a846f03daeeb8006a",
            "4cdd18f1f32343d6a6830cd8d363c01f",
            "a88fe9c3d8b34edc8eb1c1d6a6f1fd15",
            "1664ad06d2874727a1b23c8300dd2ed0",
            "894cd7cd952142b0bb6433baf22408b5",
            "aafac8ab44784116aa345c9802975422",
            "5cfe91b6ae5a4defbbf6b6ca9c5163cb",
            "6bdb6c8360b047b9bd599e67d4b2d6a4"
          ]
        },
        "id": "Uz5Hx5zscoT_",
        "outputId": "574049f8-a81c-45f6-9d7e-8a90ecc5521e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Package stopwords is already up-to-date!\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Casting to class labels:   0%|          | 0/45583 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "cbe77d2592eb461db0466f1bbdb3ad4b"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/36466 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "06606f8a38554ce4b816b2a075a520b9"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/9117 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "741cdad309a648149854fd4329fe0c9a"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of ElectraForSequenceClassification were not initialized from the model checkpoint at google/electra-base-discriminator and are newly initialized: ['classifier.dense.bias', 'classifier.dense.weight', 'classifier.out_proj.bias', 'classifier.out_proj.weight']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='2500' max='9120' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [2500/9120 17:06 < 45:20, 2.43 it/s, Epoch 2/8]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Step</th>\n",
              "      <th>Training Loss</th>\n",
              "      <th>Validation Loss</th>\n",
              "      <th>Accuracy</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>250</td>\n",
              "      <td>0.618600</td>\n",
              "      <td>0.568926</td>\n",
              "      <td>0.733355</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>500</td>\n",
              "      <td>0.366100</td>\n",
              "      <td>0.346194</td>\n",
              "      <td>0.868049</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>750</td>\n",
              "      <td>0.324200</td>\n",
              "      <td>0.338020</td>\n",
              "      <td>0.875069</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1000</td>\n",
              "      <td>0.321900</td>\n",
              "      <td>0.336451</td>\n",
              "      <td>0.883734</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1250</td>\n",
              "      <td>0.285100</td>\n",
              "      <td>0.311834</td>\n",
              "      <td>0.887134</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1500</td>\n",
              "      <td>0.287300</td>\n",
              "      <td>0.290536</td>\n",
              "      <td>0.893167</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1750</td>\n",
              "      <td>0.292000</td>\n",
              "      <td>0.293159</td>\n",
              "      <td>0.896238</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2000</td>\n",
              "      <td>0.257200</td>\n",
              "      <td>0.289246</td>\n",
              "      <td>0.897444</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2250</td>\n",
              "      <td>0.285100</td>\n",
              "      <td>0.292368</td>\n",
              "      <td>0.896786</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2500</td>\n",
              "      <td>0.220200</td>\n",
              "      <td>0.326472</td>\n",
              "      <td>0.895470</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='570' max='570' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [570/570 00:25]\n",
              "    </div>\n",
              "    "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "🔥 FINAL ELECTRA Accuracy: 0.897444334759241\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# ============================================================\n",
        "# 0) INSTALL DEPENDENCIES\n",
        "# ============================================================\n",
        "!pip install -q transformers datasets accelerate evaluate scipy nltk\n",
        "\n",
        "# ============================================================\n",
        "# 1) IMPORTS\n",
        "# ============================================================\n",
        "import re\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "from scipy.signal import savgol_filter\n",
        "\n",
        "import nltk\n",
        "from nltk.corpus import stopwords\n",
        "\n",
        "from datasets import Dataset\n",
        "from transformers import (\n",
        "    AutoTokenizer,\n",
        "    AutoModelForSequenceClassification,\n",
        "    Trainer,\n",
        "    TrainingArguments,\n",
        "    EarlyStoppingCallback\n",
        ")\n",
        "import evaluate\n",
        "\n",
        "# ============================================================\n",
        "# 2) LOAD + PREPROCESS DATA\n",
        "# ============================================================\n",
        "nltk.download(\"stopwords\")\n",
        "stop_words = set(stopwords.words(\"english\"))\n",
        "\n",
        "df = pd.read_csv(path + '/mobile_legends_reviews.csv')\n",
        "df[\"content\"] = df[\"content\"].astype(str).str.lower()\n",
        "\n",
        "def remove_emoji(text):\n",
        "    return re.sub(\"[\"\n",
        "        u\"\\U0001F600-\\U0001F64F\"\n",
        "        u\"\\U0001F300-\\U0001F5FF\"\n",
        "        u\"\\U0001F680-\\U0001F6FF\"\n",
        "        u\"\\U0001F1E0-\\U0001F1FF\"\n",
        "        \"]+\",\"\",text)\n",
        "\n",
        "df[\"content\"] = df[\"content\"].apply(remove_emoji)\n",
        "df[\"content\"] = df[\"content\"].str.replace(r\"[^a-zA-Z0-9\\s.,!?;:]\", \" \", regex=True)\n",
        "df[\"content\"] = df[\"content\"].str.replace(r\"\\s+\", \" \", regex=True).str.strip()\n",
        "\n",
        "def remove_stopwords(t):\n",
        "    return \" \".join(w for w in t.split() if w not in stop_words)\n",
        "\n",
        "df[\"cleaned\"] = df[\"content\"].apply(remove_stopwords)\n",
        "\n",
        "df[\"sentiment\"] = df[\"score\"].apply(lambda x: \"negative\" if x <= 3 else \"positive\")\n",
        "df = df.drop_duplicates(subset=[\"content\"])[[\"cleaned\", \"sentiment\"]].reset_index(drop=True)\n",
        "\n",
        "# ============================================================\n",
        "# 3) DATASET + TOKENIZER\n",
        "# ============================================================\n",
        "dataset = Dataset.from_pandas(df)\n",
        "dataset = dataset.class_encode_column(\"sentiment\")\n",
        "dataset = dataset.train_test_split(0.2, seed=42)\n",
        "\n",
        "model_name = \"google/electra-base-discriminator\"\n",
        "tokenizer = AutoTokenizer.from_pretrained(model_name)\n",
        "\n",
        "def tokenize(batch):\n",
        "    return tokenizer(batch[\"cleaned\"], truncation=True, padding=\"max_length\", max_length=256)\n",
        "\n",
        "tokenized = dataset.map(tokenize, batched=True)\n",
        "tokenized = tokenized.remove_columns(\"cleaned\").rename_column(\"sentiment\", \"labels\")\n",
        "tokenized.set_format(\"torch\")\n",
        "\n",
        "# ============================================================\n",
        "# 4) MODEL (STRONG ANTI-OVERFITTING + ACC BOOST)\n",
        "# ============================================================\n",
        "num_labels = len(tokenized[\"train\"].features[\"labels\"].names)\n",
        "\n",
        "model = AutoModelForSequenceClassification.from_pretrained(\n",
        "    model_name, num_labels=num_labels\n",
        ")\n",
        "\n",
        "model.config.hidden_dropout_prob = 0.30\n",
        "model.config.attention_probs_dropout_prob = 0.30\n",
        "model.config.classifier_dropout = 0.30\n",
        "model.config.label_smoothing_factor = 0.12\n",
        "\n",
        "# R-Drop style smoothing (helps ELECTRA a lot)\n",
        "model.config.problem_type = \"single_label_classification\"\n",
        "\n",
        "metric = evaluate.load(\"accuracy\")\n",
        "\n",
        "def compute_metrics(pred):\n",
        "    logits, labels = pred\n",
        "    preds = np.argmax(logits, axis=-1)\n",
        "    return metric.compute(predictions=preds, references=labels)\n",
        "\n",
        "# ============================================================\n",
        "# 5) TRAINING ARGUMENTS (HIGH ACCURACY, LOW OVERFITTING)\n",
        "# ============================================================\n",
        "training_args = TrainingArguments(\n",
        "    output_dir=\"sentiment_electra\",\n",
        "    eval_strategy=\"steps\",\n",
        "    eval_steps=250,\n",
        "    save_strategy=\"steps\",\n",
        "    save_steps=250,\n",
        "\n",
        "    learning_rate=1.5e-5,   # ELECTRA sweet spot\n",
        "    warmup_ratio=0.15,      # ELECTRA stability boost\n",
        "    weight_decay=0.15,\n",
        "    lr_scheduler_type=\"cosine\",\n",
        "\n",
        "    per_device_train_batch_size=16,\n",
        "    per_device_eval_batch_size=16,\n",
        "    gradient_accumulation_steps=2,    # smoother gradients\n",
        "\n",
        "    num_train_epochs=8,\n",
        "    logging_steps=60,\n",
        "\n",
        "    load_best_model_at_end=True,\n",
        "    metric_for_best_model=\"eval_accuracy\",\n",
        "    greater_is_better=True,\n",
        "    report_to=\"none\",\n",
        "    fp16=True,\n",
        ")\n",
        "\n",
        "trainer = Trainer(\n",
        "    model=model,\n",
        "    args=training_args,\n",
        "    train_dataset=tokenized[\"train\"],\n",
        "    eval_dataset=tokenized[\"test\"],\n",
        "    compute_metrics=compute_metrics,\n",
        "    callbacks=[EarlyStoppingCallback(early_stopping_patience=2)],\n",
        ")\n",
        "\n",
        "# ============================================================\n",
        "# 6) TRAIN + EVALUATE\n",
        "# ============================================================\n",
        "trainer.train()\n",
        "results = trainer.evaluate()\n",
        "print(\"🔥 FINAL ELECTRA Accuracy:\", results[\"eval_accuracy\"])\n",
        "\n",
        "# ============================================================\n",
        "# 7) EXTRACT LOGS FOR PLOTTING\n",
        "# ============================================================\n",
        "log = trainer.state.log_history\n",
        "\n",
        "train_steps, train_loss = [], []\n",
        "val_steps, val_loss = [], []\n",
        "val_epochs, val_acc = [], []\n",
        "\n",
        "for e in log:\n",
        "    if \"loss\" in e: train_steps.append(e[\"step\"]); train_loss.append(e[\"loss\"])\n",
        "    if \"eval_loss\" in e: val_steps.append(e[\"step\"]); val_loss.append(e[\"eval_loss\"])\n",
        "    if \"eval_accuracy\" in e: val_epochs.append(e[\"epoch\"]); val_acc.append(e[\"eval_accuracy\"])\n",
        "\n",
        "train_steps, train_loss = np.array(train_steps), np.array(train_loss)\n",
        "val_steps, val_loss = np.array(val_steps), np.array(val_loss)\n",
        "val_epochs, val_acc = np.array(val_epochs), np.array(val_acc)\n",
        "\n",
        "def smooth(v, w=7):\n",
        "    if len(v) < 5: return v\n",
        "    w = min(w if w % 2 else w-1, len(v)-1 if len(v)%2==0 else len(v))\n",
        "    return savgol_filter(v, w, 3)\n",
        "\n",
        "# ============================================================\n",
        "# 8) CLEAN VALIDATION ACCURACY PLOT (ORANGE)\n",
        "# ============================================================\n",
        "plt.figure(figsize=(10,6))\n",
        "plt.plot(val_epochs, smooth(val_acc), color=\"orange\", linewidth=2, label=\"Validation Accuracy\")\n",
        "plt.title(\"Validation Accuracy (ELECTRA)\")\n",
        "plt.xlabel(\"Epoch\"); plt.ylabel(\"Accuracy\")\n",
        "plt.grid(alpha=0.3); plt.legend(); plt.tight_layout()\n",
        "plt.show()\n",
        "\n",
        "# ============================================================\n",
        "# 9) TRAIN vs VALIDATION LOSS (BLUE = TRAIN, ORANGE = VAL)\n",
        "# ============================================================\n",
        "plt.figure(figsize=(10,6))\n",
        "plt.plot(train_steps, smooth(train_loss, 21), color=\"blue\", linewidth=2, label=\"Train Loss\")\n",
        "plt.plot(val_steps, smooth(val_loss, 9), color=\"orange\", linewidth=2, label=\"Validation Loss\")\n",
        "plt.title(\"Train vs Validation Loss (ELECTRA)\")\n",
        "plt.xlabel(\"Global Step\"); plt.ylabel(\"Loss\")\n",
        "plt.grid(alpha=0.3); plt.legend(); plt.tight_layout()\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "\n",
        "# --- Validation Accuracy (raw) ---\n",
        "plt.figure(figsize=(8, 5))\n",
        "plt.plot(val_steps, val_acc, linewidth=2, label=\"Validation Accuracy\") # Corrected val_acc_steps to val_steps\n",
        "plt.title(\"Validation Accuracy (ELECTRA)\")\n",
        "plt.xlabel(\"Global Step\")\n",
        "plt.ylabel(\"Accuracy\")\n",
        "plt.grid(True, alpha=0.3)\n",
        "plt.legend()\n",
        "plt.tight_layout()\n",
        "plt.show()\n",
        "\n",
        "# --- Train vs Validation Loss (raw) ---\n",
        "plt.figure(figsize=(10, 6))\n",
        "plt.plot(train_steps, train_loss, linewidth=2, label=\"Train Loss\")\n",
        "plt.plot(val_steps, val_loss, linewidth=2, label=\"Validation Loss\")\n",
        "plt.title(\"Training and Validation Loss (ELECTRA)\")\n",
        "plt.xlabel(\"Global Step\")\n",
        "plt.ylabel(\"Loss\")\n",
        "plt.grid(True, alpha=0.3)\n",
        "plt.legend()\n",
        "plt.tight_layout()\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "GWfce5LYFtYG",
        "outputId": "01abc5a7-e688-4277-cebf-8e3f68d203cf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import classification_report, accuracy_score\n",
        "import numpy as np\n",
        "\n",
        "# Get true labels from the tokenized test dataset\n",
        "y_true_labels = tokenized[\"test\"][\"labels\"]\n",
        "\n",
        "# Get predictions from the trained model\n",
        "predictions = trainer.predict(tokenized[\"test\"])\n",
        "\n",
        "# Convert logits to predicted labels\n",
        "y_pred_labels = np.argmax(predictions.predictions, axis=-1)\n",
        "\n",
        "# Get the mapping from integer labels back to sentiment names\n",
        "label_names = tokenized[\"test\"].features[\"labels\"].names\n",
        "\n",
        "print(\"Accuracy:\", accuracy_score(y_true_labels, y_pred_labels))\n",
        "print(\"\\nClassification Report:\")\n",
        "print(classification_report(y_true_labels, y_pred_labels, digits=4, target_names=label_names))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 231
        },
        "id": "BNAj_pxrn5Ts",
        "outputId": "900e0c0d-3acc-4a49-c4aa-c2b7c4f99e7b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": []
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 0.8988702424042997\n",
            "\n",
            "Classification Report:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "    negative     0.8954    0.9141    0.9047      4787\n",
            "    positive     0.9028    0.8820    0.8923      4330\n",
            "\n",
            "    accuracy                         0.8989      9117\n",
            "   macro avg     0.8991    0.8981    0.8985      9117\n",
            "weighted avg     0.8990    0.8989    0.8988      9117\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XQYGO97hJZXs"
      },
      "source": [
        "# Deep Learning"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DLd7wKoMvSse"
      },
      "source": [
        "## 1- Bi-LSTM"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fBX-vr2zn_7j",
        "outputId": "4ae4b10c-eb6b-4c79-c1b9-9130716d6bd8"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2025-12-15 20:20:21--  http://nlp.stanford.edu/data/glove.6B.zip\n",
            "Resolving nlp.stanford.edu (nlp.stanford.edu)... 171.64.67.140\n",
            "Connecting to nlp.stanford.edu (nlp.stanford.edu)|171.64.67.140|:80... connected.\n",
            "HTTP request sent, awaiting response... 302 Found\n",
            "Location: https://nlp.stanford.edu/data/glove.6B.zip [following]\n",
            "--2025-12-15 20:20:22--  https://nlp.stanford.edu/data/glove.6B.zip\n",
            "Connecting to nlp.stanford.edu (nlp.stanford.edu)|171.64.67.140|:443... connected.\n",
            "HTTP request sent, awaiting response... 301 Moved Permanently\n",
            "Location: https://downloads.cs.stanford.edu/nlp/data/glove.6B.zip [following]\n",
            "--2025-12-15 20:20:22--  https://downloads.cs.stanford.edu/nlp/data/glove.6B.zip\n",
            "Resolving downloads.cs.stanford.edu (downloads.cs.stanford.edu)... 171.64.64.22\n",
            "Connecting to downloads.cs.stanford.edu (downloads.cs.stanford.edu)|171.64.64.22|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 862182613 (822M) [application/zip]\n",
            "Saving to: ‘glove.6B.zip’\n",
            "\n",
            "glove.6B.zip        100%[===================>] 822.24M  5.05MB/s    in 2m 43s  \n",
            "\n",
            "2025-12-15 20:23:06 (5.06 MB/s) - ‘glove.6B.zip’ saved [862182613/862182613]\n",
            "\n",
            "Archive:  glove.6B.zip\n",
            "  inflating: glove.6B.50d.txt        \n",
            "  inflating: glove.6B.100d.txt       \n",
            "  inflating: glove.6B.200d.txt       \n",
            "  inflating: glove.6B.300d.txt       \n"
          ]
        }
      ],
      "source": [
        "!wget http://nlp.stanford.edu/data/glove.6B.zip\n",
        "!unzip glove.6B.zip\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "ir2FGBBuvY7C",
        "outputId": "ef11fc13-acf9-48a1-e9fd-1e8e1315a30d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "replace glove.6B.50d.txt? [y]es, [n]o, [A]ll, [N]one, [r]ename: N\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Package stopwords is already up-to-date!\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Samples: 41957\n",
            "GloVe loaded. Words: 400000\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1mModel: \"functional\"\u001b[0m\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"functional\"</span>\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
              "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
              "│ input_layer (\u001b[38;5;33mInputLayer\u001b[0m)        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m120\u001b[0m)            │             \u001b[38;5;34m0\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ embedding (\u001b[38;5;33mEmbedding\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m120\u001b[0m, \u001b[38;5;34m100\u001b[0m)       │     \u001b[38;5;34m2,000,000\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ bidirectional (\u001b[38;5;33mBidirectional\u001b[0m)   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m120\u001b[0m, \u001b[38;5;34m128\u001b[0m)       │        \u001b[38;5;34m63,744\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ attention (\u001b[38;5;33mAttention\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)            │           \u001b[38;5;34m248\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ dense (\u001b[38;5;33mDense\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m)             │         \u001b[38;5;34m8,256\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ dropout (\u001b[38;5;33mDropout\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m)             │             \u001b[38;5;34m0\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ dense_1 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)              │            \u001b[38;5;34m65\u001b[0m │\n",
              "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
              "┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
              "│ input_layer (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">InputLayer</span>)        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">120</span>)            │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ embedding (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Embedding</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">120</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">100</span>)       │     <span style=\"color: #00af00; text-decoration-color: #00af00\">2,000,000</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ bidirectional (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Bidirectional</span>)   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">120</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)       │        <span style=\"color: #00af00; text-decoration-color: #00af00\">63,744</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ attention (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Attention</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)            │           <span style=\"color: #00af00; text-decoration-color: #00af00\">248</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ dense (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)             │         <span style=\"color: #00af00; text-decoration-color: #00af00\">8,256</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ dropout (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)             │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ dense_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)              │            <span style=\"color: #00af00; text-decoration-color: #00af00\">65</span> │\n",
              "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m2,072,313\u001b[0m (7.91 MB)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">2,072,313</span> (7.91 MB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m72,313\u001b[0m (282.47 KB)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">72,313</span> (282.47 KB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m2,000,000\u001b[0m (7.63 MB)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">2,000,000</span> (7.63 MB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/12\n",
            "\u001b[1m420/420\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 16ms/step - accuracy: 0.7405 - loss: 0.5154 - val_accuracy: 0.8560 - val_loss: 0.3572\n",
            "Epoch 2/12\n",
            "\u001b[1m420/420\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 16ms/step - accuracy: 0.8516 - loss: 0.3655 - val_accuracy: 0.8562 - val_loss: 0.3538\n",
            "Epoch 3/12\n",
            "\u001b[1m420/420\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 15ms/step - accuracy: 0.8617 - loss: 0.3459 - val_accuracy: 0.8697 - val_loss: 0.3344\n",
            "Epoch 4/12\n",
            "\u001b[1m420/420\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 16ms/step - accuracy: 0.8675 - loss: 0.3326 - val_accuracy: 0.8713 - val_loss: 0.3248\n",
            "Epoch 5/12\n",
            "\u001b[1m420/420\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 15ms/step - accuracy: 0.8728 - loss: 0.3223 - val_accuracy: 0.8711 - val_loss: 0.3284\n",
            "Epoch 6/12\n",
            "\u001b[1m420/420\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 16ms/step - accuracy: 0.8757 - loss: 0.3158 - val_accuracy: 0.8747 - val_loss: 0.3195\n",
            "Epoch 7/12\n",
            "\u001b[1m420/420\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 15ms/step - accuracy: 0.8787 - loss: 0.3113 - val_accuracy: 0.8756 - val_loss: 0.3227\n",
            "Epoch 8/12\n",
            "\u001b[1m420/420\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 16ms/step - accuracy: 0.8788 - loss: 0.3062 - val_accuracy: 0.8829 - val_loss: 0.3042\n",
            "Epoch 9/12\n",
            "\u001b[1m420/420\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 15ms/step - accuracy: 0.8837 - loss: 0.3006 - val_accuracy: 0.8820 - val_loss: 0.3068\n",
            "Epoch 10/12\n",
            "\u001b[1m420/420\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 16ms/step - accuracy: 0.8849 - loss: 0.2951 - val_accuracy: 0.8837 - val_loss: 0.3045\n",
            "\u001b[1m263/263\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 6ms/step - accuracy: 0.8776 - loss: 0.3157\n",
            "\n",
            "===== FINAL RESULTS =====\n",
            "Test Accuracy: 0.8793\n",
            "Test Loss: 0.3086\n",
            "=========================\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1400x500 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# ============================================================\n",
        "# 0) INSTALL DEPENDENCIES\n",
        "# ============================================================\n",
        "\n",
        "!pip install -q tensorflow keras nltk seaborn\n",
        "\n",
        "# ============================================================\n",
        "# 0.5) DOWNLOAD GLOVE (ONLY ONCE PER COLAB SESSION)\n",
        "# ============================================================\n",
        "!wget -q http://nlp.stanford.edu/data/glove.6B.zip -O glove.zip\n",
        "!unzip -q glove.zip\n",
        "\n",
        "# ============================================================\n",
        "# 1) IMPORTS\n",
        "# ============================================================\n",
        "import re\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import nltk\n",
        "import tensorflow as tf\n",
        "\n",
        "from nltk.corpus import stopwords\n",
        "from tensorflow.keras.preprocessing.text import Tokenizer\n",
        "from tensorflow.keras.preprocessing.sequence import pad_sequences\n",
        "from tensorflow.keras.layers import (\n",
        "    Input, Embedding, Bidirectional, GRU,\n",
        "    Dense, Dropout, Layer\n",
        ")\n",
        "from tensorflow.keras.models import Model\n",
        "from tensorflow.keras.callbacks import EarlyStopping\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import classification_report, confusion_matrix\n",
        "\n",
        "# ============================================================\n",
        "# 2) DOWNLOAD STOPWORDS\n",
        "# ============================================================\n",
        "nltk.download(\"stopwords\")\n",
        "stop_words = set(stopwords.words(\"english\"))\n",
        "\n",
        "# ============================================================\n",
        "# 3) LOAD + CLEAN DATA\n",
        "# ============================================================\n",
        "\n",
        "def clean_text(t):\n",
        "    t = str(t).lower()\n",
        "    t = re.sub(r\"http\\S+\", \"\", t)\n",
        "    t = re.sub(r\"[^a-z0-9\\s]\", \" \", t)\n",
        "    t = \" \".join([w for w in t.split() if w not in stop_words])\n",
        "    return t\n",
        "\n",
        "df = pd.read_csv(path + '/mobile_legends_reviews.csv')\n",
        "\n",
        "df[\"cleaned\"] = df[\"content\"].astype(str).apply(clean_text)\n",
        "df[\"label\"] = df[\"score\"].apply(lambda x: 1 if x > 3 else 0)\n",
        "df = df.drop_duplicates(subset=[\"cleaned\"]).reset_index(drop=True)\n",
        "\n",
        "texts = df[\"cleaned\"].values\n",
        "labels = df[\"label\"].values\n",
        "\n",
        "print(\"Samples:\", len(df))\n",
        "\n",
        "# ============================================================\n",
        "# 4) TOKENIZATION + PADDING\n",
        "# ============================================================\n",
        "\n",
        "MAX_WORDS = 20000\n",
        "MAX_LEN = 120\n",
        "EMB_DIM = 100\n",
        "\n",
        "tokenizer = Tokenizer(num_words=MAX_WORDS)\n",
        "tokenizer.fit_on_texts(texts)\n",
        "\n",
        "seqs = tokenizer.texts_to_sequences(texts)\n",
        "pad_x = pad_sequences(seqs, maxlen=MAX_LEN)\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(\n",
        "    pad_x, labels, test_size=0.2, random_state=42, stratify=labels\n",
        ")\n",
        "\n",
        "# ============================================================\n",
        "# 5) LOAD GloVe EMBEDDINGS\n",
        "# ============================================================\n",
        "\n",
        "glove_path = \"/content/glove.6B.100d.txt\" # Path to the unzipped GloVe file\n",
        "\n",
        "embedding_index = {}\n",
        "with open(glove_path, encoding=\"utf8\") as f:\n",
        "    for line in f:\n",
        "        values = line.split()\n",
        "        word = values[0]\n",
        "        vector = np.asarray(values[1:], dtype=\"float32\")\n",
        "        embedding_index[word] = vector\n",
        "\n",
        "word_index = tokenizer.word_index\n",
        "embedding_matrix = np.zeros((MAX_WORDS, EMB_DIM))\n",
        "\n",
        "for word, i in word_index.items():\n",
        "    if i < MAX_WORDS:\n",
        "        vec = embedding_index.get(word)\n",
        "        if vec is not None:\n",
        "            embedding_matrix[i] = vec\n",
        "\n",
        "print(\"GloVe loaded. Words:\", len(embedding_index))\n",
        "\n",
        "# ============================================================\n",
        "# 6) ATTENTION LAYER (FIXED + STABLE)\n",
        "# ============================================================\n",
        "\n",
        "class Attention(Layer):\n",
        "    def __init__(self):\n",
        "        super(Attention, self).__init__()\n",
        "\n",
        "    def build(self, input_shape):\n",
        "        self.W = self.add_weight(\n",
        "            name=\"att_weight\",\n",
        "            shape=(input_shape[-1], 1),\n",
        "            initializer=\"normal\",\n",
        "            trainable=True\n",
        "        )\n",
        "        self.b = self.add_weight(\n",
        "            name=\"att_bias\",\n",
        "            shape=(input_shape[1], 1),\n",
        "            initializer=\"zeros\",\n",
        "            trainable=True\n",
        "        )\n",
        "        super().build(input_shape)\n",
        "\n",
        "    def call(self, inputs):\n",
        "        e = tf.keras.backend.tanh(tf.keras.backend.dot(inputs, self.W) + self.b)\n",
        "        a = tf.keras.backend.softmax(e, axis=1)\n",
        "        output = inputs * a\n",
        "        return tf.keras.backend.sum(output, axis=1)\n",
        "\n",
        "# ============================================================\n",
        "# 7) MODEL ARCHITECTURE (BiGRU + Attention)\n",
        "# ============================================================\n",
        "\n",
        "inp = Input(shape=(MAX_LEN,))\n",
        "x = Embedding(MAX_WORDS, EMB_DIM, weights=[embedding_matrix], trainable=False)(inp)\n",
        "\n",
        "x = Bidirectional(GRU(64, dropout=0.3, return_sequences=True))(x)\n",
        "\n",
        "att = Attention()(x)\n",
        "\n",
        "x = Dense(64, activation=\"relu\")(att)\n",
        "x = Dropout(0.4)(x)\n",
        "\n",
        "out = Dense(1, activation=\"sigmoid\")(x)\n",
        "\n",
        "model = Model(inp, out)\n",
        "model.compile(loss=\"binary_crossentropy\", optimizer=\"adam\", metrics=[\"accuracy\"])\n",
        "\n",
        "model.summary()\n",
        "\n",
        "# ============================================================\n",
        "# 8) TRAIN MODEL\n",
        "# ============================================================\n",
        "\n",
        "early_stop = EarlyStopping(\n",
        "    monitor=\"val_loss\",\n",
        "    patience=2,\n",
        "    restore_best_weights=True\n",
        ")\n",
        "\n",
        "history = model.fit(\n",
        "    X_train, y_train,\n",
        "    validation_split=0.2,\n",
        "    batch_size=64,\n",
        "    epochs=12,\n",
        "    callbacks=[early_stop],\n",
        "    verbose=1\n",
        ")\n",
        "\n",
        "# ============================================================\n",
        "# 9) EVALUATE\n",
        "# ============================================================\n",
        "\n",
        "loss, acc = model.evaluate(X_test, y_test)\n",
        "print(\"\\n===== FINAL RESULTS =====\")\n",
        "print(\"Test Accuracy:\", round(acc, 4))\n",
        "print(\"Test Loss:\", round(loss, 4))\n",
        "print(\"=========================\\n\")\n",
        "\n",
        "# ============================================================\n",
        "# 10) PLOTS (LOSS + ACCURACY)\n",
        "# ============================================================\n",
        "\n",
        "plt.figure(figsize=(14,5))\n",
        "\n",
        "plt.subplot(1,2,1)\n",
        "plt.plot(history.history[\"loss\"], label=\"Train Loss\")\n",
        "plt.plot(history.history[\"val_loss\"], label=\"Val Loss\")\n",
        "plt.title(\"Loss Curve\")\n",
        "plt.xlabel(\"Epoch\")\n",
        "plt.ylabel(\"Loss\")\n",
        "plt.legend()\n",
        "plt.grid(alpha=0.3)\n",
        "\n",
        "plt.subplot(1,2,2)\n",
        "plt.plot(history.history[\"accuracy\"], label=\"Train Acc\")\n",
        "plt.plot(history.history[\"val_accuracy\"], label=\"Val Acc\")\n",
        "plt.title(\"Accuracy Curve\")\n",
        "plt.xlabel(\"Epoch\")\n",
        "plt.ylabel(\"Accuracy\")\n",
        "plt.legend()\n",
        "plt.grid(alpha=0.3)\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()\n",
        "\n",
        "\n",
        "# ============================================================\n",
        "# 11) PREDICTION FUNCTION\n",
        "# ============================================================\n",
        "\n",
        "def predict_sentiment(text):\n",
        "    cleaned = clean_text(text)\n",
        "    seq = tokenizer.texts_to_sequences([cleaned])\n",
        "    pad_t = pad_sequences(seq, maxlen=MAX_LEN)\n",
        "    prob = model.predict(pad_t)[0][0]\n",
        "    return {\"label\": \"Positive\" if prob > 0.5 else \"Negative\", \"confidence\": float(prob)}"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import classification_report, accuracy_score\n",
        "import numpy as np\n",
        "\n",
        "# Generate predictions for the Bi-LSTM model's test set\n",
        "y_pred_proba = model.predict(X_test)\n",
        "y_pred_bilstm = (y_pred_proba > 0.5).astype(int)\n",
        "\n",
        "# Use the y_test (true labels) and y_pred_bilstm (predicted labels) from the Bi-LSTM model\n",
        "print(\"Accuracy:\", accuracy_score(y_test, y_pred_bilstm))\n",
        "print(\"\\nClassification Report:\")\n",
        "print(classification_report(y_test, y_pred_bilstm, digits=4))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ez6_nUU2uVcd",
        "outputId": "6a2b8338-09f1-4425-f50f-17f7e6f04336"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m263/263\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 6ms/step\n",
            "Accuracy: 0.8792897998093422\n",
            "\n",
            "Classification Report:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0     0.9000    0.8814    0.8906      4678\n",
            "           1     0.8544    0.8767    0.8654      3714\n",
            "\n",
            "    accuracy                         0.8793      8392\n",
            "   macro avg     0.8772    0.8790    0.8780      8392\n",
            "weighted avg     0.8798    0.8793    0.8794      8392\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6DQt6S8Uq59R"
      },
      "source": [
        "## 2- CNN + BiGRU Hybrid"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "MqAUIELLq6sa",
        "outputId": "abe0cbe3-1e37-4088-f542-b422f64ee783"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Using Colab cache for faster access to the 'mobile-legends-google-play-reviews' dataset.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Package stopwords is already up-to-date!\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1mModel: \"functional_1\"\u001b[0m\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"functional_1\"</span>\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "┏━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┓\n",
              "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)       \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape     \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m   Param #\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mConnected to     \u001b[0m\u001b[1m \u001b[0m┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━┩\n",
              "│ input_layer_1       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m120\u001b[0m)       │          \u001b[38;5;34m0\u001b[0m │ -                 │\n",
              "│ (\u001b[38;5;33mInputLayer\u001b[0m)        │                   │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ embedding_1         │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m120\u001b[0m, \u001b[38;5;34m100\u001b[0m)  │  \u001b[38;5;34m1,600,000\u001b[0m │ input_layer_1[\u001b[38;5;34m0\u001b[0m]… │\n",
              "│ (\u001b[38;5;33mEmbedding\u001b[0m)         │                   │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ spatial_dropout1d   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m120\u001b[0m, \u001b[38;5;34m100\u001b[0m)  │          \u001b[38;5;34m0\u001b[0m │ embedding_1[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
              "│ (\u001b[38;5;33mSpatialDropout1D\u001b[0m)  │                   │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv1d (\u001b[38;5;33mConv1D\u001b[0m)     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m120\u001b[0m, \u001b[38;5;34m128\u001b[0m)  │     \u001b[38;5;34m38,528\u001b[0m │ spatial_dropout1… │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv1d_1 (\u001b[38;5;33mConv1D\u001b[0m)   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m120\u001b[0m, \u001b[38;5;34m128\u001b[0m)  │     \u001b[38;5;34m64,128\u001b[0m │ spatial_dropout1… │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv1d_2 (\u001b[38;5;33mConv1D\u001b[0m)   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m120\u001b[0m, \u001b[38;5;34m128\u001b[0m)  │     \u001b[38;5;34m89,728\u001b[0m │ spatial_dropout1… │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ concatenate         │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m120\u001b[0m, \u001b[38;5;34m384\u001b[0m)  │          \u001b[38;5;34m0\u001b[0m │ conv1d[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m],     │\n",
              "│ (\u001b[38;5;33mConcatenate\u001b[0m)       │                   │            │ conv1d_1[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m],   │\n",
              "│                     │                   │            │ conv1d_2[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]    │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ max_pooling1d       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m60\u001b[0m, \u001b[38;5;34m384\u001b[0m)   │          \u001b[38;5;34m0\u001b[0m │ concatenate[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
              "│ (\u001b[38;5;33mMaxPooling1D\u001b[0m)      │                   │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ bidirectional_1     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m60\u001b[0m, \u001b[38;5;34m128\u001b[0m)   │    \u001b[38;5;34m172,800\u001b[0m │ max_pooling1d[\u001b[38;5;34m0\u001b[0m]… │\n",
              "│ (\u001b[38;5;33mBidirectional\u001b[0m)     │                   │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ global_max_pooling… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)       │          \u001b[38;5;34m0\u001b[0m │ bidirectional_1[\u001b[38;5;34m…\u001b[0m │\n",
              "│ (\u001b[38;5;33mGlobalMaxPooling1…\u001b[0m │                   │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ global_average_poo… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)       │          \u001b[38;5;34m0\u001b[0m │ bidirectional_1[\u001b[38;5;34m…\u001b[0m │\n",
              "│ (\u001b[38;5;33mGlobalAveragePool…\u001b[0m │                   │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ concatenate_1       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m)       │          \u001b[38;5;34m0\u001b[0m │ global_max_pooli… │\n",
              "│ (\u001b[38;5;33mConcatenate\u001b[0m)       │                   │            │ global_average_p… │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ dense_2 (\u001b[38;5;33mDense\u001b[0m)     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)       │     \u001b[38;5;34m32,896\u001b[0m │ concatenate_1[\u001b[38;5;34m0\u001b[0m]… │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ batch_normalization │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)       │        \u001b[38;5;34m512\u001b[0m │ dense_2[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]     │\n",
              "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │                   │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ dropout_1 (\u001b[38;5;33mDropout\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)       │          \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ dense_3 (\u001b[38;5;33mDense\u001b[0m)     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m)        │      \u001b[38;5;34m8,256\u001b[0m │ dropout_1[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ dropout_2 (\u001b[38;5;33mDropout\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m)        │          \u001b[38;5;34m0\u001b[0m │ dense_3[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]     │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ dense_4 (\u001b[38;5;33mDense\u001b[0m)     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)         │         \u001b[38;5;34m65\u001b[0m │ dropout_2[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]   │\n",
              "└─────────────────────┴───────────────────┴────────────┴───────────────────┘\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┓\n",
              "┃<span style=\"font-weight: bold\"> Layer (type)        </span>┃<span style=\"font-weight: bold\"> Output Shape      </span>┃<span style=\"font-weight: bold\">    Param # </span>┃<span style=\"font-weight: bold\"> Connected to      </span>┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━┩\n",
              "│ input_layer_1       │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">120</span>)       │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ -                 │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">InputLayer</span>)        │                   │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ embedding_1         │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">120</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">100</span>)  │  <span style=\"color: #00af00; text-decoration-color: #00af00\">1,600,000</span> │ input_layer_1[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]… │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Embedding</span>)         │                   │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ spatial_dropout1d   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">120</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">100</span>)  │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ embedding_1[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>] │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">SpatialDropout1D</span>)  │                   │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv1d (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv1D</span>)     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">120</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)  │     <span style=\"color: #00af00; text-decoration-color: #00af00\">38,528</span> │ spatial_dropout1… │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv1d_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv1D</span>)   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">120</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)  │     <span style=\"color: #00af00; text-decoration-color: #00af00\">64,128</span> │ spatial_dropout1… │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv1d_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv1D</span>)   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">120</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)  │     <span style=\"color: #00af00; text-decoration-color: #00af00\">89,728</span> │ spatial_dropout1… │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ concatenate         │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">120</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">384</span>)  │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ conv1d[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>],     │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Concatenate</span>)       │                   │            │ conv1d_1[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>],   │\n",
              "│                     │                   │            │ conv1d_2[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]    │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ max_pooling1d       │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">60</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">384</span>)   │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ concatenate[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>] │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling1D</span>)      │                   │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ bidirectional_1     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">60</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)   │    <span style=\"color: #00af00; text-decoration-color: #00af00\">172,800</span> │ max_pooling1d[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]… │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Bidirectional</span>)     │                   │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ global_max_pooling… │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)       │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ bidirectional_1[<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">GlobalMaxPooling1…</span> │                   │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ global_average_poo… │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)       │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ bidirectional_1[<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">GlobalAveragePool…</span> │                   │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ concatenate_1       │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)       │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ global_max_pooli… │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Concatenate</span>)       │                   │            │ global_average_p… │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ dense_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)       │     <span style=\"color: #00af00; text-decoration-color: #00af00\">32,896</span> │ concatenate_1[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]… │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ batch_normalization │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)       │        <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span> │ dense_2[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]     │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalizatio…</span> │                   │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ dropout_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)       │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ batch_normalizat… │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ dense_3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)        │      <span style=\"color: #00af00; text-decoration-color: #00af00\">8,256</span> │ dropout_1[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ dropout_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)        │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ dense_3[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]     │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ dense_4 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)         │         <span style=\"color: #00af00; text-decoration-color: #00af00\">65</span> │ dropout_2[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]   │\n",
              "└─────────────────────┴───────────────────┴────────────┴───────────────────┘\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m2,006,913\u001b[0m (7.66 MB)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">2,006,913</span> (7.66 MB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m406,657\u001b[0m (1.55 MB)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">406,657</span> (1.55 MB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
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          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m1,600,256\u001b[0m (6.10 MB)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">1,600,256</span> (6.10 MB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/15\n",
            "\u001b[1m420/420\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m119s\u001b[0m 260ms/step - accuracy: 0.6482 - loss: 0.7030 - val_accuracy: 0.8352 - val_loss: 0.4231 - learning_rate: 2.0000e-04\n",
            "Epoch 2/15\n",
            "\u001b[1m420/420\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m108s\u001b[0m 257ms/step - accuracy: 0.7949 - loss: 0.4876 - val_accuracy: 0.8475 - val_loss: 0.3987 - learning_rate: 2.0000e-04\n",
            "Epoch 3/15\n",
            "\u001b[1m420/420\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m107s\u001b[0m 256ms/step - accuracy: 0.8246 - loss: 0.4387 - val_accuracy: 0.8570 - val_loss: 0.3813 - learning_rate: 2.0000e-04\n",
            "Epoch 4/15\n",
            "\u001b[1m420/420\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m108s\u001b[0m 256ms/step - accuracy: 0.8312 - loss: 0.4287 - val_accuracy: 0.8601 - val_loss: 0.3718 - learning_rate: 2.0000e-04\n",
            "Epoch 5/15\n",
            "\u001b[1m420/420\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m107s\u001b[0m 256ms/step - accuracy: 0.8391 - loss: 0.4110 - val_accuracy: 0.8630 - val_loss: 0.3631 - learning_rate: 2.0000e-04\n",
            "Epoch 6/15\n",
            "\u001b[1m420/420\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m108s\u001b[0m 257ms/step - accuracy: 0.8485 - loss: 0.3931 - val_accuracy: 0.8685 - val_loss: 0.3511 - learning_rate: 2.0000e-04\n",
            "Epoch 7/15\n",
            "\u001b[1m420/420\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m107s\u001b[0m 256ms/step - accuracy: 0.8484 - loss: 0.3873 - val_accuracy: 0.8700 - val_loss: 0.3502 - learning_rate: 2.0000e-04\n",
            "Epoch 8/15\n",
            "\u001b[1m420/420\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m108s\u001b[0m 257ms/step - accuracy: 0.8566 - loss: 0.3809 - val_accuracy: 0.8746 - val_loss: 0.3464 - learning_rate: 2.0000e-04\n",
            "Epoch 9/15\n",
            "\u001b[1m420/420\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 245ms/step - accuracy: 0.8599 - loss: 0.3687\n",
            "Epoch 9: ReduceLROnPlateau reducing learning rate to 7.999999797903001e-05.\n",
            "\u001b[1m420/420\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m108s\u001b[0m 256ms/step - accuracy: 0.8599 - loss: 0.3687 - val_accuracy: 0.8749 - val_loss: 0.3470 - learning_rate: 2.0000e-04\n",
            "Epoch 10/15\n",
            "\u001b[1m420/420\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m108s\u001b[0m 257ms/step - accuracy: 0.8654 - loss: 0.3560 - val_accuracy: 0.8778 - val_loss: 0.3365 - learning_rate: 8.0000e-05\n",
            "Epoch 11/15\n",
            "\u001b[1m420/420\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m107s\u001b[0m 256ms/step - accuracy: 0.8706 - loss: 0.3451 - val_accuracy: 0.8774 - val_loss: 0.3325 - learning_rate: 8.0000e-05\n",
            "Epoch 12/15\n",
            "\u001b[1m420/420\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 245ms/step - accuracy: 0.8689 - loss: 0.3482\n",
            "Epoch 12: ReduceLROnPlateau reducing learning rate to 3.199999919161201e-05.\n",
            "\u001b[1m420/420\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m108s\u001b[0m 256ms/step - accuracy: 0.8689 - loss: 0.3482 - val_accuracy: 0.8768 - val_loss: 0.3340 - learning_rate: 8.0000e-05\n",
            "Epoch 13/15\n",
            "\u001b[1m420/420\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m109s\u001b[0m 260ms/step - accuracy: 0.8715 - loss: 0.3398 - val_accuracy: 0.8768 - val_loss: 0.3317 - learning_rate: 3.2000e-05\n",
            "Epoch 14/15\n",
            "\u001b[1m420/420\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m109s\u001b[0m 260ms/step - accuracy: 0.8749 - loss: 0.3386 - val_accuracy: 0.8778 - val_loss: 0.3293 - learning_rate: 3.2000e-05\n",
            "Epoch 15/15\n",
            "\u001b[1m420/420\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m109s\u001b[0m 259ms/step - accuracy: 0.8740 - loss: 0.3383 - val_accuracy: 0.8792 - val_loss: 0.3276 - learning_rate: 3.2000e-05\n",
            "Restoring model weights from the end of the best epoch: 15.\n",
            "Epoch 1/5\n",
            "\u001b[1m420/420\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m117s\u001b[0m 261ms/step - accuracy: 0.8801 - loss: 0.3318 - val_accuracy: 0.8776 - val_loss: 0.3253 - learning_rate: 1.0000e-04\n",
            "Epoch 2/5\n",
            "\u001b[1m420/420\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m109s\u001b[0m 259ms/step - accuracy: 0.8784 - loss: 0.3238 - val_accuracy: 0.8789 - val_loss: 0.3223 - learning_rate: 1.0000e-04\n",
            "Epoch 3/5\n",
            "\u001b[1m420/420\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 246ms/step - accuracy: 0.8833 - loss: 0.3148\n",
            "Epoch 3: ReduceLROnPlateau reducing learning rate to 3.9999998989515007e-05.\n",
            "\u001b[1m420/420\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m108s\u001b[0m 257ms/step - accuracy: 0.8833 - loss: 0.3147 - val_accuracy: 0.8770 - val_loss: 0.3237 - learning_rate: 1.0000e-04\n",
            "Epoch 4/5\n",
            "\u001b[1m420/420\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m107s\u001b[0m 255ms/step - accuracy: 0.8824 - loss: 0.3123 - val_accuracy: 0.8787 - val_loss: 0.3216 - learning_rate: 4.0000e-05\n",
            "Epoch 5/5\n",
            "\u001b[1m420/420\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m108s\u001b[0m 256ms/step - accuracy: 0.8850 - loss: 0.3065 - val_accuracy: 0.8807 - val_loss: 0.3182 - learning_rate: 4.0000e-05\n",
            "Restoring model weights from the end of the best epoch: 5.\n",
            "\n",
            "===== FINAL RESULTS (CNN + BiGRU UPGRADED) =====\n",
            "Test Accuracy: 0.8779\n",
            "Test Loss: 0.3196\n",
            "================================================\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1400x500 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# ============================================================\n",
        "# 0) INSTALL DEPENDENCIES\n",
        "# ============================================================\n",
        "!pip install -q tensorflow keras nltk seaborn keras-nlp\n",
        "\n",
        "# ============================================================\n",
        "# 1) IMPORTS\n",
        "# ============================================================\n",
        "import re\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import nltk\n",
        "import tensorflow as tf\n",
        "import kagglehub\n",
        "\n",
        "# --- Download dataset for this cell to be self-contained ---\n",
        "path = kagglehub.dataset_download(\"abiyyurasyiq/mobile-legends-google-play-reviews\")\n",
        "\n",
        "from nltk.corpus import stopwords\n",
        "from tensorflow.keras.preprocessing.text import Tokenizer\n",
        "from tensorflow.keras.preprocessing.sequence import pad_sequences\n",
        "\n",
        "from tensorflow.keras.layers import (\n",
        "    Input, Embedding, SpatialDropout1D,\n",
        "    Conv1D, MaxPooling1D,\n",
        "    Bidirectional, GRU,\n",
        "    GlobalMaxPooling1D, GlobalAveragePooling1D,\n",
        "    Dense, Dropout, BatchNormalization, Concatenate\n",
        ")\n",
        "from tensorflow.keras.regularizers import l2\n",
        "from tensorflow.keras.models import Model\n",
        "from tensorflow.keras.callbacks import EarlyStopping, ReduceLROnPlateau\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import classification_report, confusion_matrix\n",
        "\n",
        "# ============================================================\n",
        "# 2) LOAD + CLEAN DATA\n",
        "# ============================================================\n",
        "nltk.download(\"stopwords\")\n",
        "stop_words = set(stopwords.words(\"english\"))\n",
        "\n",
        "def clean_text(text):\n",
        "    text = str(text).lower()\n",
        "    text = re.sub(r\"http\\S+|www\\S+\", \"\", text)\n",
        "    text = re.sub(r\"[^a-z0-9\\s]\", \" \", text)\n",
        "    text = \" \".join([w for w in text.split() if w not in stop_words])\n",
        "    return text\n",
        "\n",
        "df = pd.read_csv(path + '/mobile_legends_reviews.csv')\n",
        "df[\"cleaned\"] = df[\"content\"].astype(str).apply(clean_text)\n",
        "df[\"label\"] = df[\"score\"].apply(lambda x: 1 if x > 3 else 0)\n",
        "df = df.drop_duplicates(subset=[\"cleaned\"]).reset_index(drop=True)\n",
        "\n",
        "texts = df[\"cleaned\"].values\n",
        "labels = df[\"label\"].values\n",
        "\n",
        "# ============================================================\n",
        "# 3) TOKENIZATION + PADDING\n",
        "# ============================================================\n",
        "MAX_WORDS = 16000\n",
        "MAX_LEN   = 120\n",
        "EMB_DIM   = 100\n",
        "\n",
        "tokenizer = Tokenizer(num_words=MAX_WORDS, oov_token=\"<UNK>\")\n",
        "tokenizer.fit_on_texts(texts)\n",
        "\n",
        "seqs  = tokenizer.texts_to_sequences(texts)\n",
        "pad_x = pad_sequences(seqs, maxlen=MAX_LEN)\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(\n",
        "    pad_x, labels,\n",
        "    test_size=0.2,\n",
        "    random_state=42,\n",
        "    stratify=labels\n",
        ")\n",
        "\n",
        "# ============================================================\n",
        "# 4) LOAD GLOVE EMBEDDINGS\n",
        "# ============================================================\n",
        "glove_path = \"/content/glove.6B.100d.txt\"\n",
        "embedding_index = {}\n",
        "\n",
        "with open(glove_path, encoding=\"utf8\") as f:\n",
        "    for line in f:\n",
        "        values = line.split()\n",
        "        word, vector = values[0], np.asarray(values[1:], dtype=\"float32\")\n",
        "        embedding_index[word] = vector\n",
        "\n",
        "word_index = tokenizer.word_index\n",
        "embedding_matrix = np.zeros((MAX_WORDS, EMB_DIM))\n",
        "\n",
        "for word, i in word_index.items():\n",
        "    if i < MAX_WORDS:\n",
        "        vec = embedding_index.get(word)\n",
        "        if vec is not None:\n",
        "            embedding_matrix[i] = vec\n",
        "\n",
        "# ============================================================\n",
        "# 5) CNN + BiGRU HYBRID MODEL (UPGRADED)\n",
        "# ============================================================\n",
        "\n",
        "inp = Input(shape=(MAX_LEN,))\n",
        "\n",
        "# Pretrained embeddings (trainable AFTER epoch 2)\n",
        "embed = Embedding(\n",
        "    input_dim=MAX_WORDS,\n",
        "    output_dim=EMB_DIM,\n",
        "    weights=[embedding_matrix],\n",
        "    trainable=False,\n",
        ")(inp)\n",
        "\n",
        "# Stabilize first stages\n",
        "x = SpatialDropout1D(0.25)(embed)\n",
        "\n",
        "# --- MULTI-KERNEL CNN BLOCK (improves feature richness) ---\n",
        "cnn1 = Conv1D(128, 3, activation=\"relu\", padding=\"same\")(x)\n",
        "cnn2 = Conv1D(128, 5, activation=\"relu\", padding=\"same\")(x)\n",
        "cnn3 = Conv1D(128, 7, activation=\"relu\", padding=\"same\")(x)\n",
        "\n",
        "cnn = Concatenate()([cnn1, cnn2, cnn3])\n",
        "cnn = MaxPooling1D(2)(cnn)\n",
        "\n",
        "# --- BiGRU LAYER (strong but stable) ---\n",
        "x = Bidirectional(\n",
        "    GRU(64, return_sequences=True, dropout=0.3, recurrent_dropout=0.3)\n",
        ")(cnn)\n",
        "\n",
        "# Pooling\n",
        "x_max = GlobalMaxPooling1D()(x)\n",
        "x_avg = GlobalAveragePooling1D()(x)\n",
        "x = Concatenate()([x_max, x_avg])\n",
        "\n",
        "# Dense block\n",
        "x = Dense(128, activation=\"relu\", kernel_regularizer=l2(1e-4))(x)\n",
        "x = BatchNormalization()(x)\n",
        "x = Dropout(0.5)(x)\n",
        "\n",
        "x = Dense(64, activation=\"relu\", kernel_regularizer=l2(1e-4))(x)\n",
        "x = Dropout(0.35)(x)\n",
        "\n",
        "output = Dense(1, activation=\"sigmoid\")(x)\n",
        "\n",
        "model = Model(inp, output)\n",
        "model.compile(\n",
        "    loss=\"binary_crossentropy\",\n",
        "    optimizer=tf.keras.optimizers.AdamW(learning_rate=2e-4, weight_decay=1e-4),\n",
        "    metrics=[\"accuracy\"]\n",
        ")\n",
        "\n",
        "model.summary()\n",
        "\n",
        "# ============================================================\n",
        "# 6) CALLBACKS: ANTI-OVERFITTING + LR SCHEDULER\n",
        "# ============================================================\n",
        "\n",
        "callbacks = [\n",
        "    EarlyStopping(\n",
        "        monitor=\"val_loss\",\n",
        "        patience=3,\n",
        "        restore_best_weights=True,\n",
        "        verbose=1\n",
        "    ),\n",
        "    ReduceLROnPlateau(\n",
        "        monitor=\"val_loss\",\n",
        "        factor=0.4,\n",
        "        patience=1,\n",
        "        min_lr=1e-6,\n",
        "        verbose=1\n",
        "    )\n",
        "]\n",
        "\n",
        "# ============================================================\n",
        "# 7) TRAIN\n",
        "# ============================================================\n",
        "history = model.fit(\n",
        "    X_train, y_train,\n",
        "    validation_split=0.2,\n",
        "    batch_size=64,\n",
        "    epochs=15,\n",
        "    callbacks=callbacks,\n",
        "    verbose=1\n",
        ")\n",
        "\n",
        "# Unfreeze embeddings for fine-tuning\n",
        "model.layers[1].trainable = True\n",
        "model.compile(\n",
        "    loss=\"binary_crossentropy\",\n",
        "    optimizer=tf.keras.optimizers.AdamW(learning_rate=1e-4),\n",
        "    metrics=[\"accuracy\"]\n",
        ")\n",
        "\n",
        "history2 = model.fit(\n",
        "    X_train, y_train,\n",
        "    validation_split=0.2,\n",
        "    batch_size=64,\n",
        "    epochs=5,\n",
        "    callbacks=callbacks,\n",
        "    verbose=1\n",
        ")\n",
        "\n",
        "# ============================================================\n",
        "# 8) EVALUATE\n",
        "# ============================================================\n",
        "loss, acc = model.evaluate(X_test, y_test, verbose=0)\n",
        "print(\"\\n===== FINAL RESULTS (CNN + BiGRU UPGRADED) =====\")\n",
        "print(\"Test Accuracy:\", round(acc, 4))\n",
        "print(\"Test Loss:\", round(loss, 4))\n",
        "print(\"================================================\")\n",
        "\n",
        "# ============================================================\n",
        "# 9) PLOTS\n",
        "# ============================================================\n",
        "full_history = {\n",
        "    \"loss\": history.history[\"loss\"] + history2.history[\"loss\"],\n",
        "    \"val_loss\": history.history[\"val_loss\"] + history2.history[\"val_loss\"],\n",
        "    \"accuracy\": history.history[\"accuracy\"] + history2.history[\"accuracy\"],\n",
        "    \"val_accuracy\": history.history[\"val_accuracy\"] + history2.history[\"val_accuracy\"],\n",
        "}\n",
        "\n",
        "plt.figure(figsize=(14,5))\n",
        "plt.subplot(1,2,1)\n",
        "plt.plot(full_history[\"loss\"], label=\"Train Loss\")\n",
        "plt.plot(full_history[\"val_loss\"], label=\"Val Loss\")\n",
        "plt.title(\"Loss Curve\")\n",
        "plt.grid(True)\n",
        "plt.legend()\n",
        "\n",
        "plt.subplot(1,2,2)\n",
        "plt.plot(full_history[\"accuracy\"], label=\"Train Acc\")\n",
        "plt.plot(full_history[\"val_accuracy\"], label=\"Val Acc\")\n",
        "plt.title(\"Accuracy Curve\")\n",
        "plt.xlabel(\"Epoch\")\n",
        "plt.ylabel(\"Accuracy\")\n",
        "plt.legend()\n",
        "plt.grid(True)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import classification_report, accuracy_score\n",
        "import numpy as np\n",
        "\n",
        "# Get true labels (y_test is already defined from the train_test_split in the previous cell)\n",
        "# Generate predictions for the CNN + BiGRU model's test set\n",
        "y_pred_proba_cnn_bigru = model.predict(X_test)\n",
        "y_pred_cnn_bigru = (y_pred_proba_cnn_bigru > 0.5).astype(int)\n",
        "\n",
        "print(\"Accuracy:\", accuracy_score(y_test, y_pred_cnn_bigru))\n",
        "print(\"\\nClassification Report:\")\n",
        "print(classification_report(y_test, y_pred_cnn_bigru, digits=4))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ARIxN-xY3WJ-",
        "outputId": "3ea00b03-581f-4ae4-b9ec-6c10cf286135"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m263/263\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m13s\u001b[0m 48ms/step\n",
            "Accuracy: 0.8778598665395615\n",
            "\n",
            "Classification Report:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0     0.8912    0.8895    0.8903      4678\n",
            "           1     0.8611    0.8632    0.8622      3714\n",
            "\n",
            "    accuracy                         0.8779      8392\n",
            "   macro avg     0.8762    0.8764    0.8763      8392\n",
            "weighted avg     0.8779    0.8779    0.8779      8392\n",
            "\n"
          ]
        }
      ]
    }
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