tfidf_SVM.ipynb

{
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   "cell_type": "code",
   "execution_count": 1,
   "id": "d9ed1f34-3207-4bcc-bf41-aaf4b6ec5283",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.svm import SVC\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": "39ee8f12-1177-4264-91f6-87f051418b84",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Step 1: Create a small dataset with clear sentiment labels\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": "5cb93c64-fb68-4ec2-a7d9-a1050caf36dd",
   "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": "7553cad3-a1d6-4cbe-965b-e6df84bcd231",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Step 3: Convert text data into a TF-IDF representation\n",
    "vectorizer = TfidfVectorizer()\n",
    "X = vectorizer.fit_transform(df['text'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "273ef643-4042-4c2c-a1e7-d7dd0082fd96",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TF-IDF Matrix:\n",
      "[[0.         0.         0.         0.         0.         0.\n",
      "  0.         0.         0.         0.         0.         0.\n",
      "  0.78223376 0.5172355  0.         0.         0.         0.\n",
      "  0.34724312 0.         0.         0.         0.        ]\n",
      " [0.         0.         0.         0.         0.         0.\n",
      "  0.         0.         0.         0.         0.         0.5537218\n",
      "  0.         0.43070289 0.         0.         0.6513674  0.\n",
      "  0.28914994 0.         0.         0.         0.        ]\n",
      " [0.         0.         0.         0.62626343 0.         0.\n",
      "  0.37188828 0.         0.         0.         0.         0.\n",
      "  0.         0.         0.         0.62626343 0.         0.\n",
      "  0.27800598 0.         0.         0.         0.        ]\n",
      " [0.         0.66338198 0.         0.         0.         0.\n",
      "  0.39393005 0.         0.         0.         0.         0.56393529\n",
      "  0.         0.         0.         0.         0.         0.\n",
      "  0.29448336 0.         0.         0.         0.        ]\n",
      " [0.         0.         0.         0.         0.57735027 0.57735027\n",
      "  0.         0.         0.         0.         0.         0.\n",
      "  0.         0.         0.         0.         0.         0.\n",
      "  0.         0.         0.         0.57735027 0.        ]\n",
      " [0.         0.         0.         0.         0.         0.\n",
      "  0.47701743 0.         0.         0.         0.80330193 0.\n",
      "  0.         0.         0.         0.         0.         0.\n",
      "  0.35659553 0.         0.         0.         0.        ]\n",
      " [0.         0.         0.         0.         0.         0.\n",
      "  0.         0.         0.6513674  0.         0.         0.\n",
      "  0.         0.43070289 0.         0.         0.         0.\n",
      "  0.28914994 0.         0.5537218  0.         0.        ]\n",
      " [0.         0.         0.         0.         0.         0.\n",
      "  0.29415538 0.49536048 0.         0.         0.         0.\n",
      "  0.         0.         0.49536048 0.         0.         0.49536048\n",
      "  0.         0.         0.42110166 0.         0.        ]\n",
      " [0.42087897 0.         0.         0.         0.         0.\n",
      "  0.         0.         0.         0.49509851 0.         0.\n",
      "  0.         0.3273734  0.         0.         0.         0.\n",
      "  0.21978027 0.49509851 0.         0.         0.42087897]\n",
      " [0.49121065 0.         0.57783278 0.         0.         0.\n",
      "  0.34312915 0.         0.         0.         0.         0.\n",
      "  0.         0.         0.         0.         0.         0.\n",
      "  0.25650702 0.         0.         0.         0.49121065]]\n"
     ]
    }
   ],
   "source": [
    "# Print the TF-IDF matrix\n",
    "print(\"TF-IDF Matrix:\")\n",
    "print(X.toarray())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "afcd5d2d-983a-4d17-95b4-60b10130eb49",
   "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": "efb8e51f-be0c-4db8-865f-66b9b33d9fb9",
   "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": "71388496-6624-4e44-a79d-126b6be04317",
   "metadata": {},
   "outputs": [
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      ],
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       "SVC(kernel='linear', random_state=42)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Step 5: Train an SVM classifier\n",
    "clf = SVC(kernel='linear', random_state=42)\n",
    "clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "ef9701c6-0718-4d58-86d5-238a8519c67c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Predict on test data\n",
    "y_pred = clf.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "e59051aa-c9fe-4e70-9527-905993cedf18",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Evaluate the classifier\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "report = classification_report(y_test, y_pred)\n",
    "conf_matrix = confusion_matrix(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "de62c137-231f-4925-9d66-901195c8184d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.0\n",
      "\n",
      "Classification Report:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "    negative       0.00      0.00      0.00       1.0\n",
      "    positive       0.00      0.00      0.00       1.0\n",
      "\n",
      "    accuracy                           0.00       2.0\n",
      "   macro avg       0.00      0.00      0.00       2.0\n",
      "weighted avg       0.00      0.00      0.00       2.0\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(f'Accuracy: {accuracy}\\n')\n",
    "print('Classification Report:')\n",
    "print(report)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "565b826e-21cb-4adc-97c2-3bb5295919ca",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrix:\n",
      "[[0 1]\n",
      " [1 0]]\n"
     ]
    }
   ],
   "source": [
    "print('Confusion Matrix:')\n",
    "print(conf_matrix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "3ff03802-d1a9-4049-89f9-27f698a2410d",
   "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": "1ec06bf6-ea4b-46b3-ae30-36e2371dce41",
   "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": "6de53c21-2f10-45a2-9df2-f519ddaadf91",
   "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": "e8602d2a-e316-4649-a5c6-96a5aaa364c2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sample Input 1: \"I absolutely loved this film, it was fantastic!\"\n",
      "Classification: positive\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: negative\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": "3e709aa6-e1f0-41ea-855b-dbd8e61f7da9",
   "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
}