{ "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": "72edb0f7-c46c-4128-919c-912662aaab70", "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) (4.60.1)\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": "c689585e-42c4-4bc2-a989-ed151b538558" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Using Colab cache for faster access to the 'mobile-legends-google-play-reviews' dataset.\n", "Path to dataset files: /kaggle/input/mobile-legends-google-play-reviews\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": "1717042a-57df-4529-9436-66c584cefb71" }, "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. 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0d9b3706c-a29d-4661-b757-812be9802f33A Google userLove this game. Honestly didn't have any expec...502025-06-10 13:20:23
1cc717825-ea2b-444c-8e80-e4f517314880A Google userthe graphics? 10/10!!! this game is so funn!! 😄502025-06-10 13:19:39
2f3c5353d-65f8-486f-81b0-5ac733ed88a0A Google userHorrible. your matchmaking sucks. The players ...102025-06-10 13:18:52
35d2611d3-dfda-41b5-87e8-d20498b0f4a4A Google userdark system. fix your match making, it makes t...102025-06-10 13:15:51
438f96ac5-c264-4045-babf-543f576d70b2A Google userThis game has a so much dark sistem and the li...102025-06-10 13:11:34
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0d9b3706c-a29d-4661-b757-812be9802f33A Google userlove this game. honestly didn't have any expec...502025-06-10 13:20:23love game. honestly expectations going knew hu...[love, game, ., honestly, expectations, going,...[love, game, ., honestly, expectation, going, ...
1cc717825-ea2b-444c-8e80-e4f517314880A Google userthe graphics? 10/10!!! this game is so funn!!502025-06-10 13:19:39graphics? 10/10!!! game funn!![graphics, ?, 10/10, !, !, !, game, funn, !, !][graphic, ?, 10/10, !, !, !, game, funn, !, !]
2f3c5353d-65f8-486f-81b0-5ac733ed88a0A Google userhorrible. your matchmaking sucks. the players ...102025-06-10 13:18:52horrible. matchmaking sucks. players either re...[horrible, ., matchmaking, sucks, ., players, ...[horrible, ., matchmaking, suck, ., player, ei...
35d2611d3-dfda-41b5-87e8-d20498b0f4a4A Google userdark system. fix your match making, it makes t...102025-06-10 13:15:51dark system. fix match making, makes game enjo...[dark, system, ., fix, match, making, ,, makes...[dark, system, ., fix, match, making, ,, make,...
438f96ac5-c264-4045-babf-543f576d70b2A Google userthis game has a so much dark sistem and the li...102025-06-10 13:11:34game much dark sistem line always bad lost acc...[game, much, dark, sistem, line, always, bad, ...[game, much, dark, sistem, line, always, bad, ...
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" ] }, "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": "b05e99e6-166f-49a6-f843-0f3ab320983b" }, "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", "
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reviewIduserNamecontentscorethumbsUpCountatcleanedtokenslemmatized_tokens
0d9b3706c-a29d-4661-b757-812be9802f33A Google userlove this game. honestly didnt have any expect...502025-06-10 13:20:23love game. honestly expectations going knew hu...[love, game, ., honestly, expectations, going,...[love, game, ., honestly, expectation, going, ...
1cc717825-ea2b-444c-8e80-e4f517314880A Google userthe graphics? 1010!!! this game is so funn!!502025-06-10 13:19:39graphics? 10/10!!! game funn!![graphics, ?, 10/10, !, !, !, game, funn, !, !][graphic, ?, 10/10, !, !, !, game, funn, !, !]
2f3c5353d-65f8-486f-81b0-5ac733ed88a0A Google userhorrible. your matchmaking sucks. the players ...102025-06-10 13:18:52horrible. matchmaking sucks. players either re...[horrible, ., matchmaking, sucks, ., players, ...[horrible, ., matchmaking, suck, ., player, ei...
35d2611d3-dfda-41b5-87e8-d20498b0f4a4A Google userdark system. fix your match making, it makes t...102025-06-10 13:15:51dark system. fix match making, makes game enjo...[dark, system, ., fix, match, making, ,, makes...[dark, system, ., fix, match, making, ,, make,...
438f96ac5-c264-4045-babf-543f576d70b2A Google userthis game has a so much dark sistem and the li...102025-06-10 13:11:34game much dark sistem line always bad lost acc...[game, much, dark, sistem, line, always, bad, ...[game, much, dark, sistem, line, always, bad, ...
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reviewIduserNamecontentscorethumbsUpCountatcleanedtokenslemmatized_tokens
0d9b3706c-a29d-4661-b757-812be9802f33A Google userlove this game. honestly didnt have any expect...502025-06-10 13:20:23love game. honestly expectations going knew hu...[love, game, ., honestly, expectations, going,...[love, game, ., honestly, expectation, going, ...
1cc717825-ea2b-444c-8e80-e4f517314880A Google userthe graphics? 1010!!! this game is so funn!!502025-06-10 13:19:39graphics? 10/10!!! game funn!![graphics, ?, 10/10, !, !, !, game, funn, !, !][graphic, ?, 10/10, !, !, !, game, funn, !, !]
2f3c5353d-65f8-486f-81b0-5ac733ed88a0A Google userhorrible. your matchmaking sucks. the players ...102025-06-10 13:18:52horrible. matchmaking sucks. players either re...[horrible, ., matchmaking, sucks, ., players, ...[horrible, ., matchmaking, suck, ., player, ei...
35d2611d3-dfda-41b5-87e8-d20498b0f4a4A Google userdark system. fix your match making, it makes t...102025-06-10 13:15:51dark system. fix match making, makes game enjo...[dark, system, ., fix, match, making, ,, makes...[dark, system, ., fix, match, making, ,, make,...
438f96ac5-c264-4045-babf-543f576d70b2A Google userthis game has a so much dark sistem and the li...102025-06-10 13:11:34game much dark sistem line always bad lost acc...[game, much, dark, sistem, line, always, bad, ...[game, much, dark, sistem, line, always, bad, ...
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\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": "a82cd07e-6394-47bd-b9ab-a0bba7fa73a5" }, "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[31m36.2 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=938d3e9feed2471c7ec6386fdd31b549accde97c9aa526bb0116388648f003c0\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": [ "relabel sentiment\n", "def label_sentiment(rating):\n", " if rating in [1, 2, 3]:\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": "105bcbe1-4f37-463e-c7a2-816242aadc94" }, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "
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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": "b61c23a1-082c-4e3b-e97a-fce13041acdd" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "✅ Sentiment column created.\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "
" ], "image/png": 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\n" 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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": "1157c5eb-92d2-488c-e1cd-d5dc297b8301" }, "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": 196 }, "id": "Ats1VlPbCQtI", "outputId": "5779d171-af3b-4af3-c91d-e01f2160972d" }, "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": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
count
sentiment
negative22036
positive21857
\n", "

" ] }, "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": "571c08be-df3f-4d8b-b00b-c3d1d06ea2d4" }, "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": [ "
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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": "31b00031-17f8-4dd5-f5d5-40b12ad5803e" }, "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": "c883ce2f-7af3-4984-e0c1-62053749f4ac" }, "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": "6c23c88c-013a-4772-ae00-d792d08e13c5" }, "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": "b44b94d6-0864-4454-fb59-e460a0ae1b38" }, "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": "4c4ec68a-07e4-4179-bb4d-e7bdccc0a2be" }, "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 0.107195 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": "cba78cfb-d7e8-4783-80d8-121aed065781" }, "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 <= 3 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": { "colab": { "base_uri": "https://localhost:8080/", "height": 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StepTraining LossValidation LossAccuracy
1000.6715000.6401050.530433
2000.4436000.3549440.866170
3000.3163000.3079390.883036
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5000.3134000.2716080.895625
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8000.2845000.2569550.902469
9000.2748000.2564930.901980
10000.2542000.2568910.901980

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\n" 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\n" 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StepTraining LossValidation LossAccuracy
3500.6141000.4161780.846248
7000.3147000.2872470.893669
10500.2950000.2599480.906747
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17500.2743000.2687020.909558
21000.2419000.2561140.913346
24500.2415000.2610090.911146
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\n", " \n", " \n", " [512/512 00:33]\n", "
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\n" 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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": "markdown", "metadata": { "id": "uHaAMD7gF-s9" }, "source": [ "## 3-Roberta-Base" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000, "referenced_widgets": [ "3a90daef7b334819a4fe7f6e214ffd3e", "7ae00b7b65e64e62b631e16885e52b13", "b6674d573a67443fb607c3d5c2ddb387", "9f29ac255d924845b2fbb02d5867256d", "87ca38fbf9f14a7cb1f964215c4caead", "3475bcdb8a0e421793112f1ab8178cde", "f45cd22b440b46f7b60ccbf216fb6259", "381a98a767774204961060c882daf592", "8c40822edfce483686aaab711850ed1d", "c0a3d298d5604bcb8423bb4aa39471d5", "4439e49cdd654ecbb42327c37549d2ae", "d0c6349ba1394afe9a2bc5cd2c5311cb", "a043a9a5548b4b019910b4d66dcbf937", "4af83ae6a9d94e61946fc775d2d5d67a", "cce3a2bd298b41149203078a8842c6a8", "fbf73a8fabb2421492384736b85c67ac", "ba4ed4011d8244a1a738f7f9b9debe91", "b929c58bf1594ca2a3c87c8c4eec93ab", "2a279cb1d0584a3285232ba3888a1d85", "f12bb9c4b7d14f4e8d4564b78e6f3dfa", "430d7ad60d10489097d7e507cf3c8470", "f13f21bd7cd84a369cc70e845781ee24", 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"d2e674b6358f4eeeaeb32359aa49d133", "1837166bd652406f98ab16fd89e6afd1", "b68633f6681b4b2ca3fffedcbcceb818", "9cfb90f2213d4d2dbe9eddd69ef98c15", "6197ab84a451444cafcc5bf8f5ac5096", "0a9653f110164b0e91831c447120afd3", "2bdf279d473f4cf583b364e8d932c626", "0ff59236445a4889831a7622dca55be3", "64d05d8b70fb4afea219228e297c84bf", "7e1b33a8386b439594e1f8791ee59750", "af040ef45a5a4e7690024a558f8594ee", "8adf0a1c92524f1c88328cf838cd1624", "7690204b75e54e93b06a2eda69bc0687", "4ba601a1894f4196a9bc5f468ff35d71", "c15109377cfe4a4c866e05e8bed4bda1", "d1dfb64312194d42af4710aa5351acef", "c2051be070174c72b6d968d2ab95945d", "70eb90face9e462c93434e6e426ac96f", "8257600baf8a4a2f8b72af68a6343a28", "b3223e6a5f5c4ae982db93ce8b734d29", "23e66ad341604796a7f4d9964e84cd67", "8a092f7ac36e4ba0a293899c3d0f2d84", "30a3e441da4a42ae879072562882bb1b" ] }, "id": "6WV2CWfPGAOC", "outputId": "81cf2cf6-a28b-48bf-9746-7ff77c7516db" }, "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" ], "text/html": [ "\n", "
\n", " \n", " \n", " [2100/9096 09:29 < 31:38, 3.69 it/s, Epoch 1/8]\n", "
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StepTraining LossValidation LossAccuracy
3000.4159000.3310870.869379
6000.3356000.2859530.895217
9000.3232000.2775680.894997
12000.2889000.2723540.900715
15000.2894000.2630610.904893
18000.2925000.2666580.900825
21000.2772000.2914280.900055

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\n", " \n", " \n", " [569/569 00:14]\n", "
\n", " " ] }, "metadata": {} }, { "output_type": "stream", "name": "stdout", "text": [ "🔥 FINAL ACCURACY: 0.9048927982407916\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "
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\n" 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}, "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": "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": [ "fdc7216de8fe413c891b1ea7b261ad91", "c4cd6fc6a3a44a2583cfd961d8db5693", "3f17af9eef6a47c0a35122b69d9025a2", "1a8fa3e5a4214f388315d94d54b96139", "a0fe2ea872474da49bb0eae543d6ea9e", "c61cd3f7e02f4d0080708d1ad22a5372", "5c2db1ccda82413988c3cb09e62d9a17", "ee2bff8d1574433998ee98ead03db086", "6c58a7b576e8421a9bc698185c3c7461", "500c46d1b75349aaac763a62c83c6f42", "23b8aa918f7b41a08f4b015ef9b2f227", "7deb2d85af064053b755100a92a28ae4", "92852eaf7ee5489b9f4b577cc9841e59", "d676e68b1fb0475191f3f316372f8598", "1b81d6a224be45c193dea24297782246", "bb73313806014a14a341c2c93437c393", "f8188456ba994180b9ac93d007846814", "2ac8a552d21545acbe856dc6a3d8bae2", "c29515bbc9c046249494a5541e4cabf3", "1fd4f6117035467393568c1d50ec90ae", "9cf2ae7884e34333b4046aa0dcaaf551", "912e10bb1f9a4d6bb27f583ecb96e203", "d4e025b16c9749b8a6fbf485a4312b62", "b7d4af97c7964e848661b4b05a7bdbbb", "f1b44f166a164dc2b61133b152c58057", "202b15ae87aa495b84844e3a27b335c6", "a1f187416eb24fb08b412164f8402a89", "c1fef37ef09b40e2bfea8a093f8a0aa4", "39d23843f9df4c61a85b7d1183c8d0be", "382ce57caa724f6c9fb52337ede1ec80", "fae49d3fa2f54066a95e24ca63e69ee3", 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3000.4178000.3350390.866850
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\n" 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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": "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": [ "68612e3ad95a4128baf8b0ef2ca1c1f6", "210d5a4594344069a78355d77e9566c0", "d8299bfbca0142b9ac50f927ce872f3e", "51cbc4842a5c4e1e99148ccc79afd95d", "85d3746a04274d48ba6c9837473db109", "876a2d7a5bd9485184e0992c400245e5", "b4630565ed61415b8f43c87411142bb9", "1cbd36f2eb5f4e3e88370695a38651f2", "89dd4e4e22044ba0b281020da58a6609", "f18916e245dd4c828e5c7d10461c8181", "9f2de788431d4c4fa183fb658f1ea372", "61b462f0b6634178b0fc0cf49ebde87c", "117111961e9046dfa16b80fbba31a5b1", "ea352210957d43d2b59519fbd7eaca33", "cdc834c9c78b4da18f5f7e4ae7fbe693", "64b7f3c1229c4d76bda3d68658d43362", "c1495135499d48efa65949b0cb3905f5", "72dfdf82b8324c039aa0d55c2b0e0776", "eabad2b9014744dab0db8f57c7752109", "c0a4673be5ac41778f2c4d03897577d5", "a90e43fa59e14f3e9fc3c36854057ebd", "d9a356cc04b448c2aa2bcd7b54fe91e2", "15dbc7269eda4d98b8509368d65418db", "d86bdd0ad86847819a23330c9104d776", "b0cf420aeb304d82a1ddd76e96c03b70", "7434349552f6425aa6bdcddc798536b6", "f81b8d33491b474393d50158d2b34845", "2e43fa49a5764b859898885b43365078", "c267a891312546b382b001d3cd22b6f6", "a51ac9c04d914d36897fcda9bdc776a1", "e71d771e01bd44c58e30624a15e43013", "393005ee8f9042cea1ef4f0d0bc332ea", "9c52b25766b94b7fba0d8c73e446f00b" ] }, "id": "Uz5Hx5zscoT_", "outputId": "804fcc1e-226d-41af-c3f0-eca3d88d4401" }, "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" ], "text/html": [ "\n", "
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StepTraining LossValidation LossAccuracy
2500.6189000.5508260.783810
5000.3685000.3560530.866294
7500.3339000.3462000.870242
10000.3227000.3401740.880662
12500.2879000.3201150.889657
15000.2876000.2917220.888340
17500.2932000.2904810.892618
20000.2562000.2870950.895251
22500.2854000.2874250.896567
25000.2247000.3152960.897225
27500.2156000.2993000.895141
30000.2518000.2838200.897225

" ] }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ "" ], "text/html": [ "\n", "

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\n", " " ] }, "metadata": {} }, { "output_type": "stream", "name": "stdout", "text": [ "🔥 FINAL ELECTRA Accuracy: 0.8972249643523089\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "
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\n" 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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=(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\"); 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 (Smoothed)\")\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": "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": "925d4ed8-bf5e-42f6-9d28-dcc56e901bc3" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "--2025-12-01 18:35:21-- 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", "--2025-12-01 18:35:21-- 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", "--2025-12-01 18:35:22-- 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 5.13MB/s in 2m 55s \n", "\n", "2025-12-01 18:38:18 (4.70 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": "ceaf8d78-7b47-465f-c973-d27f9e1ae980" }, "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_5\"\u001b[0m\n" ], "text/html": [ "
Model: \"functional_5\"\n",
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              "┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃\n",
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              "│ input_layer_5 (InputLayer)      │ (None, 120)            │             0 │\n",
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              "│ bidirectional_5 (Bidirectional) │ (None, 120, 128)       │        63,744 │\n",
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              "│ attention_2 (Attention)         │ (None, 128)            │           248 │\n",
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              "│ dense_10 (Dense)                │ (None, 64)             │         8,256 │\n",
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              "│ dropout_9 (Dropout)             │ (None, 64)             │             0 │\n",
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              "│ dense_11 (Dense)                │ (None, 1)              │            65 │\n",
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\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[1m10s\u001b[0m 17ms/step - accuracy: 0.7279 - loss: 0.5242 - val_accuracy: 0.8543 - val_loss: 0.3596\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: 0.8517 - loss: 0.3688 - val_accuracy: 0.8650 - val_loss: 0.3436\n", "Epoch 3/12\n", "\u001b[1m420/420\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 16ms/step - accuracy: 0.8595 - loss: 0.3480 - val_accuracy: 0.8644 - val_loss: 0.3396\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: 0.8653 - loss: 0.3349 - val_accuracy: 0.8685 - val_loss: 0.3279\n", "Epoch 5/12\n", "\u001b[1m420/420\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 17ms/step - accuracy: 0.8726 - loss: 0.3274 - val_accuracy: 0.8671 - val_loss: 0.3365\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: 0.8736 - loss: 0.3220 - val_accuracy: 0.8734 - val_loss: 0.3236\n", "Epoch 7/12\n", "\u001b[1m420/420\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 17ms/step - accuracy: 0.8764 - loss: 0.3124 - val_accuracy: 0.8783 - val_loss: 0.3150\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: 0.8790 - loss: 0.3070 - val_accuracy: 0.8762 - val_loss: 0.3206\n", "Epoch 9/12\n", "\u001b[1m420/420\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 16ms/step - accuracy: 0.8811 - loss: 0.3005 - val_accuracy: 0.8820 - val_loss: 0.3095\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: 0.8856 - loss: 0.2958 - val_accuracy: 0.8808 - val_loss: 0.3089\n", "Epoch 11/12\n", "\u001b[1m420/420\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 16ms/step - accuracy: 0.8860 - loss: 0.2924 - val_accuracy: 0.8752 - val_loss: 0.3259\n", "Epoch 12/12\n", "\u001b[1m420/420\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 16ms/step - accuracy: 0.8889 - loss: 0.2852 - val_accuracy: 0.8831 - val_loss: 0.3074\n", "\u001b[1m263/263\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 7ms/step - accuracy: 0.8726 - loss: 0.3229\n", "\n", "===== FINAL RESULTS =====\n", "Test Accuracy: 0.8779\n", "Test Loss: 0.3105\n", "=========================\n", "\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "
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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": "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": "1b5d682f-6085-4fa4-9cd7-0f24e3a6d783" }, "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": [ "\u001b[1mModel: \"functional_2\"\u001b[0m\n" ], "text/html": [ "
Model: \"functional_2\"\n",
              "
\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_2 │ (\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_2 │ (\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_2[\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_2[\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_2 │ (\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_2[\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_2[\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_3 (\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_3[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ │ │ │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ dropout_3 (\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_4 (\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_3[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ dropout_4 (\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_4[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", "│ dense_5 (\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_4[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", "└─────────────────────┴───────────────────┴────────────┴───────────────────┘\n" ], "text/html": [ "
┏━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┓\n",
              "┃ Layer (type)        ┃ Output Shape      ┃    Param # ┃ Connected to      ┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━┩\n",
              "│ input_layer_2       │ (None, 120)       │          0 │ -                 │\n",
              "│ (InputLayer)        │                   │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ embedding_2         │ (None, 120, 100)  │  1,600,000 │ input_layer_2[0]… │\n",
              "│ (Embedding)         │                   │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ spatial_dropout1d   │ (None, 120, 100)  │          0 │ embedding_2[0][0] │\n",
              "│ (SpatialDropout1D)  │                   │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv1d (Conv1D)     │ (None, 120, 128)  │     38,528 │ spatial_dropout1… │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv1d_1 (Conv1D)   │ (None, 120, 128)  │     64,128 │ spatial_dropout1… │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ conv1d_2 (Conv1D)   │ (None, 120, 128)  │     89,728 │ spatial_dropout1… │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ concatenate         │ (None, 120, 384)  │          0 │ conv1d[0][0],     │\n",
              "│ (Concatenate)       │                   │            │ conv1d_1[0][0],   │\n",
              "│                     │                   │            │ conv1d_2[0][0]    │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ max_pooling1d       │ (None, 60, 384)   │          0 │ concatenate[0][0] │\n",
              "│ (MaxPooling1D)      │                   │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ bidirectional_2     │ (None, 60, 128)   │    172,800 │ max_pooling1d[0]… │\n",
              "│ (Bidirectional)     │                   │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ global_max_pooling… │ (None, 128)       │          0 │ bidirectional_2[… │\n",
              "│ (GlobalMaxPooling1… │                   │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ global_average_poo… │ (None, 128)       │          0 │ bidirectional_2[… │\n",
              "│ (GlobalAveragePool… │                   │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ concatenate_1       │ (None, 256)       │          0 │ global_max_pooli… │\n",
              "│ (Concatenate)       │                   │            │ global_average_p… │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ dense_3 (Dense)     │ (None, 128)       │     32,896 │ concatenate_1[0]… │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ batch_normalization │ (None, 128)       │        512 │ dense_3[0][0]     │\n",
              "│ (BatchNormalizatio… │                   │            │                   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ dropout_3 (Dropout) │ (None, 128)       │          0 │ batch_normalizat… │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ dense_4 (Dense)     │ (None, 64)        │      8,256 │ dropout_3[0][0]   │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ dropout_4 (Dropout) │ (None, 64)        │          0 │ dense_4[0][0]     │\n",
              "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
              "│ dense_5 (Dense)     │ (None, 1)         │         65 │ dropout_4[0][0]   │\n",
              "└─────────────────────┴───────────────────┴────────────┴───────────────────┘\n",
              "
\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": [ "
 Total params: 2,006,913 (7.66 MB)\n",
              "
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 Trainable params: 406,657 (1.55 MB)\n",
              "
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 Non-trainable params: 1,600,256 (6.10 MB)\n",
              "
\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[1m118s\u001b[0m 260ms/step - accuracy: 0.6625 - loss: 0.6917 - val_accuracy: 0.8297 - val_loss: 0.4192 - 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 258ms/step - accuracy: 0.7963 - loss: 0.4857 - val_accuracy: 0.8533 - val_loss: 0.3896 - learning_rate: 2.0000e-04\n", "Epoch 3/15\n", "\u001b[1m420/420\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m108s\u001b[0m 257ms/step - accuracy: 0.8161 - loss: 0.4552 - val_accuracy: 0.8546 - val_loss: 0.3773 - learning_rate: 2.0000e-04\n", "Epoch 4/15\n", "\u001b[1m420/420\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 248ms/step - accuracy: 0.8306 - loss: 0.4267\n", "Epoch 4: ReduceLROnPlateau reducing learning rate to 7.999999797903001e-05.\n", "\u001b[1m420/420\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m109s\u001b[0m 260ms/step - accuracy: 0.8306 - loss: 0.4267 - val_accuracy: 0.8446 - val_loss: 0.3944 - learning_rate: 2.0000e-04\n", "Epoch 5/15\n", "\u001b[1m420/420\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m109s\u001b[0m 259ms/step - accuracy: 0.8384 - loss: 0.4170 - val_accuracy: 0.8567 - val_loss: 0.3740 - learning_rate: 8.0000e-05\n", "Epoch 6/15\n", "\u001b[1m420/420\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m109s\u001b[0m 259ms/step - accuracy: 0.8413 - loss: 0.4086 - val_accuracy: 0.8592 - val_loss: 0.3676 - learning_rate: 8.0000e-05\n", "Epoch 7/15\n", "\u001b[1m420/420\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m108s\u001b[0m 258ms/step - accuracy: 0.8454 - loss: 0.4022 - val_accuracy: 0.8658 - val_loss: 0.3563 - learning_rate: 8.0000e-05\n", "Epoch 8/15\n", "\u001b[1m420/420\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 247ms/step - accuracy: 0.8442 - loss: 0.3972\n", "Epoch 8: ReduceLROnPlateau reducing learning rate to 3.199999919161201e-05.\n", "\u001b[1m420/420\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m109s\u001b[0m 259ms/step - accuracy: 0.8443 - loss: 0.3972 - val_accuracy: 0.8661 - val_loss: 0.3568 - learning_rate: 8.0000e-05\n", "Epoch 9/15\n", "\u001b[1m420/420\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 247ms/step - accuracy: 0.8510 - loss: 0.3903\n", "Epoch 9: ReduceLROnPlateau reducing learning rate to 1.2799999967683107e-05.\n", "\u001b[1m420/420\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m108s\u001b[0m 258ms/step - accuracy: 0.8510 - loss: 0.3903 - val_accuracy: 0.8664 - val_loss: 0.3566 - learning_rate: 3.2000e-05\n", "Epoch 10/15\n", "\u001b[1m420/420\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 246ms/step - accuracy: 0.8515 - loss: 0.3876\n", "Epoch 10: ReduceLROnPlateau reducing learning rate to 5.120000059832819e-06.\n", "\u001b[1m420/420\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m108s\u001b[0m 258ms/step - accuracy: 0.8515 - loss: 0.3876 - val_accuracy: 0.8622 - val_loss: 0.3622 - learning_rate: 1.2800e-05\n", "Epoch 10: early stopping\n", "Restoring model weights from the end of the best epoch: 7.\n", "Epoch 1/5\n", "\u001b[1m420/420\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m118s\u001b[0m 262ms/step - accuracy: 0.8497 - loss: 0.3888 - val_accuracy: 0.8686 - val_loss: 0.3535 - 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: 0.8534 - loss: 0.3816 - val_accuracy: 0.8735 - val_loss: 0.3376 - 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 247ms/step - accuracy: 0.8590 - loss: 0.3693\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[1m109s\u001b[0m 258ms/step - accuracy: 0.8590 - loss: 0.3693 - val_accuracy: 0.8683 - val_loss: 0.3492 - learning_rate: 1.0000e-04\n", "Epoch 4/5\n", "\u001b[1m420/420\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m109s\u001b[0m 259ms/step - accuracy: 0.8627 - loss: 0.3551 - val_accuracy: 0.8725 - val_loss: 0.3361 - learning_rate: 4.0000e-05\n", "Epoch 5/5\n", "\u001b[1m420/420\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m109s\u001b[0m 259ms/step - accuracy: 0.8671 - loss: 0.3508 - val_accuracy: 0.8759 - val_loss: 0.3312 - 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: 0.8743\n", "Test Loss: 0.3318\n", "================================================\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "
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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=\"\")\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", 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