language_model.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "eb32fb53a2522022",
   "metadata": {},
   "source": [
    "<h1>Review Classification using spaCy</h1>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b55c9e3cdf3d36be",
   "metadata": {},
   "source": [
    "**Import Dependencies**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "d33bbfad29c49c5b",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-14T11:36:20.021894115Z",
     "start_time": "2023-08-14T11:36:19.967623385Z"
    }
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split, GridSearchCV\n",
    "from sklearn.feature_extraction.text import CountVectorizer, TfidfTransformer\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.naive_bayes import MultinomialNB\n",
    "from sklearn.metrics import accuracy_score, classification_report\n",
    "import spacy\n",
    "from matplotlib import pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9086a4d84f39c9a2",
   "metadata": {},
   "source": [
    "**Load the spaCy English model**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "62eef703e1d90136",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-14T11:15:17.198486523Z",
     "start_time": "2023-08-14T11:15:16.526410644Z"
    }
   },
   "outputs": [],
   "source": [
    "nlp = spacy.load(\"en_core_web_sm\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7c3ada9ad881fa96",
   "metadata": {},
   "source": [
    "**Load the dataset**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "9c25655ff2aac591",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-14T11:15:17.932666794Z",
     "start_time": "2023-08-14T11:15:17.202299319Z"
    }
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"datasets/IMDB Dataset.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "23b6c4eb98829860",
   "metadata": {},
   "source": [
    "**Convert labels to binary: positive -> 1, negative -> 0**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "18ed7a8c85a7a9d4",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-14T11:15:17.950950362Z",
     "start_time": "2023-08-14T11:15:17.941971169Z"
    }
   },
   "outputs": [],
   "source": [
    "df[\"sentiment\"] = df[\"sentiment\"].map({\"positive\": 1, \"negative\": 0})"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3fba88746d117e39",
   "metadata": {},
   "source": [
    "**Divide the dataset into training, validation, and test sets**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "4752502cadb4cf1c",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-14T11:15:17.986627647Z",
     "start_time": "2023-08-14T11:15:17.952922691Z"
    }
   },
   "outputs": [],
   "source": [
    "train_df, test_df = train_test_split(df, test_size=0.2, random_state=42)\n",
    "train_df, val_df = train_test_split(train_df, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1b661cb9e8aed207",
   "metadata": {},
   "source": [
    "**Create a text classification pipeline**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "1766676912d5ef74",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-14T11:15:17.987096284Z",
     "start_time": "2023-08-14T11:15:17.973216093Z"
    }
   },
   "outputs": [],
   "source": [
    "base_model = Pipeline(\n",
    "    [\n",
    "        (\"vectorizer\", CountVectorizer()),\n",
    "        (\"tfidf\", TfidfTransformer()),\n",
    "        (\"classifier\", MultinomialNB()),\n",
    "    ]\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "**Train the base model with default hyperparameters**"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "f9293ba596c958f1"
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "395627915adf9e7e",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-14T11:15:23.514922386Z",
     "start_time": "2023-08-14T11:15:17.978196641Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "Pipeline(steps=[('vectorizer', CountVectorizer()),\n                ('tfidf', TfidfTransformer()),\n                ('classifier', MultinomialNB())])",
      "text/html": "<style>#sk-container-id-1 {color: black;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;vectorizer&#x27;, CountVectorizer()),\n                (&#x27;tfidf&#x27;, TfidfTransformer()),\n                (&#x27;classifier&#x27;, MultinomialNB())])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;vectorizer&#x27;, CountVectorizer()),\n                (&#x27;tfidf&#x27;, TfidfTransformer()),\n                (&#x27;classifier&#x27;, MultinomialNB())])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">CountVectorizer</label><div class=\"sk-toggleable__content\"><pre>CountVectorizer()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">TfidfTransformer</label><div class=\"sk-toggleable__content\"><pre>TfidfTransformer()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">MultinomialNB</label><div class=\"sk-toggleable__content\"><pre>MultinomialNB()</pre></div></div></div></div></div></div></div>"
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "base_model.fit(train_df[\"review\"], train_df[\"sentiment\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9813f944f5faeaba",
   "metadata": {},
   "source": [
    "**Evaluate the base model on the test set**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "b43a57100fb04b70",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-14T11:15:24.645227645Z",
     "start_time": "2023-08-14T11:15:23.545789675Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Accuracy (Base Model): 0.8608\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.84      0.89      0.86      4961\n",
      "           1       0.89      0.83      0.86      5039\n",
      "\n",
      "    accuracy                           0.86     10000\n",
      "   macro avg       0.86      0.86      0.86     10000\n",
      "weighted avg       0.86      0.86      0.86     10000\n"
     ]
    }
   ],
   "source": [
    "test_predictions = base_model.predict(test_df[\"review\"])\n",
    "test_accuracy = accuracy_score(test_df[\"sentiment\"], test_predictions)\n",
    "print(\"Test Accuracy (Base Model):\", test_accuracy)\n",
    "print(classification_report(test_df[\"sentiment\"], test_predictions))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "31f31f6144221d63",
   "metadata": {},
   "source": [
    "**Evaluate the base model on the validation set**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "2dd51badb2078340",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-14T11:15:25.557450857Z",
     "start_time": "2023-08-14T11:15:24.658055812Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation Accuracy (Base Model): 0.863\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.84      0.90      0.87      3959\n",
      "           1       0.89      0.83      0.86      4041\n",
      "\n",
      "    accuracy                           0.86      8000\n",
      "   macro avg       0.86      0.86      0.86      8000\n",
      "weighted avg       0.87      0.86      0.86      8000\n"
     ]
    }
   ],
   "source": [
    "val_base_predictions = base_model.predict(val_df[\"review\"])\n",
    "val_base_accuracy = accuracy_score(val_df[\"sentiment\"], val_base_predictions)\n",
    "print(\"Validation Accuracy (Base Model):\", val_base_accuracy)\n",
    "print(classification_report(val_df[\"sentiment\"], val_base_predictions))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae77ca88358e08e2",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "a8bfb626ebae7dd2",
   "metadata": {},
   "source": [
    "**Define hyperparameters**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "43172e445c251247",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-14T11:15:25.568213107Z",
     "start_time": "2023-08-14T11:15:25.558526279Z"
    }
   },
   "outputs": [],
   "source": [
    "param_grid = {\n",
    "    \"vectorizer__ngram_range\": [(1, 1), (1, 2)],  # Uni-gram and Bi-gram\n",
    "    \"tfidf__use_idf\": [True, False],\n",
    "    \"classifier__alpha\": [0.1, 0.01, 0.001, 0.0001],\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "57e185d5f947700e",
   "metadata": {},
   "source": [
    "**Perform hyperparameter tuning**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "2cf12293d261dfcc",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-14T11:23:32.284147786Z",
     "start_time": "2023-08-14T11:15:25.562126756Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Hyperparameters: {'classifier__alpha': 0.1, 'tfidf__use_idf': True, 'vectorizer__ngram_range': (1, 2)}\n"
     ]
    }
   ],
   "source": [
    "grid_search = GridSearchCV(base_model, param_grid, cv=3, scoring=\"accuracy\")\n",
    "grid_search.fit(train_df[\"review\"], train_df[\"sentiment\"])\n",
    "print(\"Best Hyperparameters:\", grid_search.best_params_)\n",
    "best_model = grid_search.best_estimator_"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7259a39232ecdbb9",
   "metadata": {},
   "source": [
    "**Evaluate the tuned model on the test set**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "f1aea1261260d21d",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-14T11:23:35.906080413Z",
     "start_time": "2023-08-14T11:23:32.266796018Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Accuracy (Tuned Model): 0.8933\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.88      0.90      0.89      4961\n",
      "           1       0.90      0.88      0.89      5039\n",
      "\n",
      "    accuracy                           0.89     10000\n",
      "   macro avg       0.89      0.89      0.89     10000\n",
      "weighted avg       0.89      0.89      0.89     10000\n"
     ]
    }
   ],
   "source": [
    "test_predictions = best_model.predict(test_df[\"review\"])\n",
    "test_accuracy = accuracy_score(test_df[\"sentiment\"], test_predictions)\n",
    "print(\"Test Accuracy (Tuned Model):\", test_accuracy)\n",
    "print(classification_report(test_df[\"sentiment\"], test_predictions))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "28d692332d791ede",
   "metadata": {},
   "source": [
    "**Evaluate the tuned model on the validation set**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "209e49960a69ca93",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-14T11:23:38.983432739Z",
     "start_time": "2023-08-14T11:23:35.923331470Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation Accuracy (Tuned Model): 0.89725\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.88      0.91      0.90      3959\n",
      "           1       0.91      0.88      0.90      4041\n",
      "\n",
      "    accuracy                           0.90      8000\n",
      "   macro avg       0.90      0.90      0.90      8000\n",
      "weighted avg       0.90      0.90      0.90      8000\n"
     ]
    }
   ],
   "source": [
    "val_predictions = best_model.predict(val_df[\"review\"])\n",
    "val_accuracy = accuracy_score(val_df[\"sentiment\"], val_predictions)\n",
    "print(\"Validation Accuracy (Tuned Model):\", val_accuracy)\n",
    "print(classification_report(val_df[\"sentiment\"], val_predictions))"
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "**Visualization of hyperparameter tuning**"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "1eecba171e49340f"
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 2 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize hyperparameter search results using a heatmap\n",
    "results = pd.DataFrame(grid_search.cv_results_)\n",
    "pivot_results = results.pivot_table(\n",
    "    index=\"param_vectorizer__ngram_range\",\n",
    "    columns=[\"param_tfidf__use_idf\", \"param_classifier__alpha\"],\n",
    "    values=\"mean_test_score\",\n",
    ")\n",
    "sns.heatmap(pivot_results, annot=True, cmap=\"binary_r\")\n",
    "plt.title(\"Hyperparameter Search Results\")\n",
    "plt.xlabel(\"TF-IDF Use IDF\")\n",
    "plt.ylabel(\"Ngram Range\")\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-08-14T11:51:09.433982426Z",
     "start_time": "2023-08-14T11:51:09.162229451Z"
    }
   },
   "id": "67199b03cc70b235"
  },
  {
   "cell_type": "markdown",
   "id": "d6bdada3a577b7ed",
   "metadata": {},
   "source": [
    "### Model testing"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "438b22bb2f5ace09",
   "metadata": {},
   "source": [
    "**Predict sentiment using the trained model**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "e98e56b5b8dc8c06",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-14T11:36:28.065555450Z",
     "start_time": "2023-08-14T11:36:28.004814573Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted Sentiment: positive\n"
     ]
    }
   ],
   "source": [
    "# Input review\n",
    "review = \"This movie was great.\"\n",
    "\n",
    "predicted_sentiment = best_model.predict([review])[0]\n",
    "predicted_sentiment_label = \"positive\" if predicted_sentiment == 1 else \"negative\"\n",
    "print(\"Predicted Sentiment:\", predicted_sentiment_label)"
   ]
  }
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