{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "9a942010-3e2c-44c2-a7c7-c81b9c4f5ad4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "==== Naive Bayes with BoW ====\n",
      "Accuracy: 0.8307692307692308\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "    negative       0.94      0.28      0.44       120\n",
      "    positive       0.82      0.99      0.90       400\n",
      "\n",
      "    accuracy                           0.83       520\n",
      "   macro avg       0.88      0.64      0.67       520\n",
      "weighted avg       0.85      0.83      0.79       520\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "==== Naive Bayes with TF-IDF ====\n",
      "Accuracy: 0.7711538461538462\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "    negative       1.00      0.01      0.02       120\n",
      "    positive       0.77      1.00      0.87       400\n",
      "\n",
      "    accuracy                           0.77       520\n",
      "   macro avg       0.89      0.50      0.44       520\n",
      "weighted avg       0.82      0.77      0.67       520\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "==== Logistic Regression with TF-IDF ====\n",
      "Accuracy: 0.8134615384615385\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "    negative       0.90      0.22      0.35       120\n",
      "    positive       0.81      0.99      0.89       400\n",
      "\n",
      "    accuracy                           0.81       520\n",
      "   macro avg       0.85      0.60      0.62       520\n",
      "weighted avg       0.83      0.81      0.77       520\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Import all the  libraries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer\n",
    "from sklearn.naive_bayes import MultinomialNB\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import accuracy_score, classification_report, confusion_matrix\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import re\n",
    "\n",
    "# Load the dataset\n",
    "file_path = r\"C:\\Users\\X1 YOGA\\Downloads\\unbalanceddataset.csv\"\n",
    "df = pd.read_csv(file_path)\n",
    "\n",
    "# Features and labels\n",
    "X = df[\"text\"].astype(str)\n",
    "y = df[\"sentiment\"]\n",
    "\n",
    "#  Apply data preprocessing\n",
    "def clean_text(text):\n",
    "    text = text.lower()                                # lowercasing\n",
    "    text = re.sub(r'[^a-zA-Z\\s]', '', text)           # remove punctuation/numbers\n",
    "    text = re.sub(r'\\s+', ' ', text).strip()          # remove extra spaces\n",
    "    return text\n",
    "\n",
    "X_clean = [clean_text(doc) for doc in X]\n",
    "\n",
    "# Split the dataset\n",
    "X_train, X_test, y_train, y_test = train_test_split(X_clean, y, test_size=0.2, random_state=42, stratify=y)\n",
    "\n",
    "#  Apply feature representation methods\n",
    "# Bag-of-Words\n",
    "vectorizer_bow = CountVectorizer(max_features=5000)\n",
    "X_train_bow = vectorizer_bow.fit_transform(X_train)\n",
    "X_test_bow = vectorizer_bow.transform(X_test)\n",
    "\n",
    "# TF-IDF\n",
    "vectorizer_tfidf = TfidfVectorizer(max_features=5000)\n",
    "X_train_tfidf = vectorizer_tfidf.fit_transform(X_train)\n",
    "X_test_tfidf = vectorizer_tfidf.transform(X_test)\n",
    "\n",
    "# Helper function to plot confusion matrix\n",
    "def plot_confusion_matrix(y_true, y_pred, title):\n",
    "    cm = confusion_matrix(y_true, y_pred, labels=np.unique(y))\n",
    "    plt.figure(figsize=(6,5))\n",
    "    sns.heatmap(cm, annot=True, fmt='d', cmap='Blues', xticklabels=np.unique(y), yticklabels=np.unique(y))\n",
    "    plt.xlabel(\"Predicted\")\n",
    "    plt.ylabel(\"Actual\")\n",
    "    plt.title(title)\n",
    "    plt.show()\n",
    "\n",
    "#  Train and evaluate models\n",
    "print(\" Naive Bayes with BoW \")\n",
    "nb_bow = MultinomialNB()\n",
    "nb_bow.fit(X_train_bow, y_train)\n",
    "y_pred_nb_bow = nb_bow.predict(X_test_bow)\n",
    "print(\"Accuracy:\", accuracy_score(y_test, y_pred_nb_bow))\n",
    "print(classification_report(y_test, y_pred_nb_bow))\n",
    "plot_confusion_matrix(y_test, y_pred_nb_bow, \"Naive Bayes (BoW)\")\n",
    "\n",
    "print(\" Naive Bayes with TF-IDF \")\n",
    "nb_tfidf = MultinomialNB()\n",
    "nb_tfidf.fit(X_train_tfidf, y_train)\n",
    "y_pred_nb_tfidf = nb_tfidf.predict(X_test_tfidf)\n",
    "print(\"Accuracy:\", accuracy_score(y_test, y_pred_nb_tfidf))\n",
    "print(classification_report(y_test, y_pred_nb_tfidf))\n",
    "plot_confusion_matrix(y_test, y_pred_nb_tfidf, \"Naive Bayes (TF-IDF)\")\n",
    "\n",
    "print(\" Logistic Regression with TF-IDF \")\n",
    "logreg = LogisticRegression(max_iter=1000)\n",
    "logreg.fit(X_train_tfidf, y_train)\n",
    "y_pred_logreg = logreg.predict(X_test_tfidf)\n",
    "print(\"Accuracy:\", accuracy_score(y_test, y_pred_logreg))\n",
    "print(classification_report(y_test, y_pred_logreg))\n",
    "plot_confusion_matrix(y_test, y_pred_logreg, \"Logistic Regression (TF-IDF)\")\n"
   ]
  },
  {
   "cell_type": "code",
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