BoW_RF.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "19d2f7ae-4514-4bd2-94cb-f98cb2c9792a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import accuracy_score, classification_report, confusion_matrix\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from textblob import TextBlob"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "cd6c0311-8283-470a-bd96-51a7c09fa957",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Step 1: Create a small dataset\n",
    "data = {\n",
    "    'text': [\n",
    "        'I love this movie', \n",
    "        'This movie is terrible', \n",
    "        'I really enjoyed this film', \n",
    "        'This film is awful', \n",
    "        'What a fantastic experience', \n",
    "        'I hated this film', \n",
    "        'This was a great movie', \n",
    "        'The film was not good', \n",
    "        'I am very happy with this movie', \n",
    "        'I am disappointed with this film'\n",
    "    ],\n",
    "    'label': ['positive', 'negative', 'positive', 'negative', 'positive', 'negative', 'positive', 'negative', 'positive', 'negative']\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e27cfe25-7deb-4186-8e62-ed8bfd3a4f54",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create DataFrame\n",
    "df = pd.DataFrame(data)\n",
    "\n",
    "# Step 2: Preprocess the text data\n",
    "df['text'] = df['text'].str.lower()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "d3c55bda-c950-4fe2-99c3-a2e533e144a4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Step 3: Convert text data into a BoW representation\n",
    "vectorizer = CountVectorizer()\n",
    "X = vectorizer.fit_transform(df['text'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "753c5367-905f-46e7-8c51-eec2c2afa1ac",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Bag of Words Vectors (Sparse Matrix):\n",
      "[[0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 1 0 0 0 0]\n",
      " [0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 1 0 1 0 0 0 0]\n",
      " [0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0]\n",
      " [0 1 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0]\n",
      " [0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0]\n",
      " [0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0]\n",
      " [0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 1 0 1 0 0]\n",
      " [0 0 0 0 0 0 1 1 0 0 0 0 0 0 1 0 0 1 0 0 1 0 0]\n",
      " [1 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 1 1 0 0 1]\n",
      " [1 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1]]\n"
     ]
    }
   ],
   "source": [
    "# Print the Bag of Words vectors\n",
    "print(\"Bag of Words Vectors (Sparse Matrix):\")\n",
    "print(X.toarray())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "041fd534-f2df-4ca5-92e5-462d444336b5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Feature Names (Words):\n",
      "['am' 'awful' 'disappointed' 'enjoyed' 'experience' 'fantastic' 'film'\n",
      " 'good' 'great' 'happy' 'hated' 'is' 'love' 'movie' 'not' 'really'\n",
      " 'terrible' 'the' 'this' 'very' 'was' 'what' 'with']\n"
     ]
    }
   ],
   "source": [
    "# Print the feature names (words)\n",
    "print(\"Feature Names (Words):\")\n",
    "print(vectorizer.get_feature_names_out())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "f3e75f6a-0a6f-42f1-a5b0-6776d28f270e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Step 4: Split data into train and test sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, df['label'], test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "9837d8ee-c77c-4d0d-b76b-e2ed389bd9bd",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      ],
      "text/plain": [
       "RandomForestClassifier(random_state=42)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Step 5: Train a Random Forest classifier\n",
    "clf = RandomForestClassifier(n_estimators=100, random_state=42)\n",
    "clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "3422d389-5298-4b45-bd1d-83c184a8826f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Predict on test data\n",
    "y_pred = clf.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "5f85de0f-5df0-4933-81d6-74ccf0053084",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Evaluate the classifier\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "report = classification_report(y_test, y_pred, zero_division=1)\n",
    "conf_matrix = confusion_matrix(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "08938d60-a2bc-4593-8f17-ab579dead6a1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.5\n",
      "\n",
      "Classification Report:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "    negative       1.00      0.00      0.00         1\n",
      "    positive       0.50      1.00      0.67         1\n",
      "\n",
      "    accuracy                           0.50         2\n",
      "   macro avg       0.75      0.50      0.33         2\n",
      "weighted avg       0.75      0.50      0.33         2\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(f'Accuracy: {accuracy}\\n')\n",
    "print('Classification Report:')\n",
    "print(report)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "5dae3d29-7aa2-44a0-8fa9-b49563477048",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrix:\n",
      "[[0 1]\n",
      " [0 1]]\n"
     ]
    }
   ],
   "source": [
    "print('Confusion Matrix:')\n",
    "print(conf_matrix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "2105e830-9c4f-4f4f-954e-d5f8a2f863b1",
   "metadata": {},
   "outputs": [
    {
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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting the confusion matrix\n",
    "sns.heatmap(conf_matrix, annot=True, fmt='d', cmap='Blues', xticklabels=['negative', 'positive'], yticklabels=['negative', 'positive'])\n",
    "plt.xlabel('Predicted')\n",
    "plt.ylabel('Actual')\n",
    "plt.title('Confusion Matrix')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "67c5c5b4-f8b0-4474-81b9-5a5eab9ad79a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function to classify new user input and get polarity and subjectivity\n",
    "def classify_and_analyze_input(new_input):\n",
    "    new_input_lower = new_input.lower()\n",
    "    new_input_vector = vectorizer.transform([new_input_lower])\n",
    "    prediction = clf.predict(new_input_vector)[0]\n",
    "\n",
    "    blob = TextBlob(new_input)\n",
    "    polarity = blob.sentiment.polarity\n",
    "    subjectivity = blob.sentiment.subjectivity\n",
    "\n",
    "    return prediction, polarity, subjectivity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "da44dae9-799c-4fa5-9406-54c5d24ff600",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Test the classification function with two sample inputs\n",
    "sample_inputs = [\n",
    "    \"I absolutely loved this film, it was fantastic!\",\n",
    "    \"The movie was not good and I did not enjoy it at all.\"\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "cdf2fde7-37e3-4205-84d3-3809d7c7a110",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sample Input 1: \"I absolutely loved this film, it was fantastic!\"\n",
      "Classification: negative\n",
      "Polarity: 0.6\n",
      "Subjectivity: 0.8500000000000001\n",
      "\n",
      "Sample Input 2: \"The movie was not good and I did not enjoy it at all.\"\n",
      "Classification: positive\n",
      "Polarity: -0.275\n",
      "Subjectivity: 0.55\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for i, sample in enumerate(sample_inputs):\n",
    "    classification, polarity, subjectivity = classify_and_analyze_input(sample)\n",
    "    print(f'Sample Input {i+1}: \"{sample}\"')\n",
    "    print(f'Classification: {classification}')\n",
    "    print(f'Polarity: {polarity}')\n",
    "    print(f'Subjectivity: {subjectivity}\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "086e44a1-3a3b-4ea8-b520-d071b7d0eedf",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.19"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}