Sentiment Analyser/RNN(w2v).ipynb

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     "text": [
      "Epoch 1/15\n",
      "196/196 [==============================] - 59s 272ms/step - loss: 0.6775 - acc: 0.5654 - val_loss: 0.6538 - val_acc: 0.6173 - lr: 0.0010\n",
      "Epoch 2/15\n",
      "196/196 [==============================] - 52s 265ms/step - loss: 0.6585 - acc: 0.6066 - val_loss: 0.6501 - val_acc: 0.6184 - lr: 0.0010\n",
      "Epoch 3/15\n",
      "196/196 [==============================] - 55s 280ms/step - loss: 0.6488 - acc: 0.6216 - val_loss: 0.6378 - val_acc: 0.6365 - lr: 0.0010\n",
      "Epoch 4/15\n",
      "196/196 [==============================] - 58s 294ms/step - loss: 0.6427 - acc: 0.6304 - val_loss: 0.6505 - val_acc: 0.6307 - lr: 0.0010\n",
      "Epoch 5/15\n",
      "196/196 [==============================] - ETA: 0s - loss: 0.6337 - acc: 0.6417\n",
      "Epoch 5: ReduceLROnPlateau reducing learning rate to 0.00010000000474974513.\n",
      "196/196 [==============================] - 57s 291ms/step - loss: 0.6337 - acc: 0.6417 - val_loss: 0.6780 - val_acc: 0.6249 - lr: 0.0010\n",
      "Epoch 6/15\n",
      "196/196 [==============================] - 55s 280ms/step - loss: 0.6225 - acc: 0.6509 - val_loss: 0.6244 - val_acc: 0.6507 - lr: 1.0000e-04\n",
      "Epoch 7/15\n",
      "196/196 [==============================] - 55s 280ms/step - loss: 0.6213 - acc: 0.6510 - val_loss: 0.6274 - val_acc: 0.6496 - lr: 1.0000e-04\n",
      "Epoch 8/15\n",
      "196/196 [==============================] - 55s 281ms/step - loss: 0.6181 - acc: 0.6549 - val_loss: 0.6220 - val_acc: 0.6522 - lr: 1.0000e-04\n",
      "Epoch 9/15\n",
      "196/196 [==============================] - 56s 285ms/step - loss: 0.6180 - acc: 0.6551 - val_loss: 0.6195 - val_acc: 0.6536 - lr: 1.0000e-04\n",
      "Epoch 10/15\n",
      "196/196 [==============================] - 56s 284ms/step - loss: 0.6165 - acc: 0.6585 - val_loss: 0.6242 - val_acc: 0.6512 - lr: 1.0000e-04\n",
      "Epoch 11/15\n",
      "196/196 [==============================] - 56s 287ms/step - loss: 0.6176 - acc: 0.6549 - val_loss: 0.6187 - val_acc: 0.6538 - lr: 1.0000e-04\n",
      "Epoch 12/15\n",
      "196/196 [==============================] - 56s 285ms/step - loss: 0.6148 - acc: 0.6585 - val_loss: 0.6178 - val_acc: 0.6574 - lr: 1.0000e-04\n",
      "Epoch 13/15\n",
      "196/196 [==============================] - 56s 287ms/step - loss: 0.6160 - acc: 0.6599 - val_loss: 0.6160 - val_acc: 0.6574 - lr: 1.0000e-04\n",
      "Epoch 14/15\n",
      "196/196 [==============================] - 54s 275ms/step - loss: 0.6142 - acc: 0.6572 - val_loss: 0.6156 - val_acc: 0.6571 - lr: 1.0000e-04\n",
      "Epoch 15/15\n",
      "196/196 [==============================] - 54s 274ms/step - loss: 0.6136 - acc: 0.6595 - val_loss: 0.6162 - val_acc: 0.6576 - lr: 1.0000e-04\n",
      "196/196 [==============================] - 7s 35ms/step - loss: 0.6162 - acc: 0.6576\n",
      "Test score: 0.6162243485450745\n",
      "Test accuracy: 0.6576399803161621\n"
     ]
    }
   ],
   "source": [
    "from keras.datasets import imdb\n",
    "from keras.models import Sequential\n",
    "from keras.layers import LSTM, Dense, Embedding, Dropout\n",
    "from keras.callbacks import EarlyStopping, ReduceLROnPlateau\n",
    "from keras.preprocessing.text import Tokenizer\n",
    "from gensim.models import Word2Vec\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from tensorflow.keras.preprocessing.sequence import pad_sequences \n",
    "import pickle\n",
    "# Load the IMDB dataset and split it into training and test sets\n",
    "(x_train, y_train), (x_test, y_test) = imdb.load_data(num_words=10000)\n",
    "\n",
    "# Tokenize the text and convert it to sequences\n",
    "tokenizer = Tokenizer(num_words=10000)\n",
    "x_train_str = [str(text) for text in x_train]\n",
    "tokenizer.fit_on_texts(x_train_str)\n",
    "x_train = tokenizer.texts_to_sequences(x_train_str)\n",
    "x_test_str = [str(text) for text in x_test]\n",
    "x_test = tokenizer.texts_to_sequences(x_test_str)\n",
    "\n",
    "# Pad the sequences to a fixed length\n",
    "maxlen = 100\n",
    "x_train = pad_sequences(x_train, maxlen=maxlen)\n",
    "x_test = pad_sequences(x_test, maxlen=maxlen)\n",
    "\n",
    "# Load pre-trained Word2Vec model\n",
    "w2v_model = Word2Vec.load('w2v_model.bin')\n",
    "\n",
    "# Create embedding matrix\n",
    "word_index = tokenizer.word_index\n",
    "embedding_matrix = np.zeros((len(word_index) + 1, 100))\n",
    "for word, i in word_index.items():\n",
    "    if word in w2v_model.wv.key_to_index:\n",
    "        embedding_matrix[i] = w2v_model.wv[word]\n",
    "\n",
    "# Define the model architecture\n",
    "model = Sequential()\n",
    "model.add(Embedding(len(word_index) + 1, 100, weights=[embedding_matrix], input_length=maxlen, trainable=False))\n",
    "model.add(Dropout(0.2))\n",
    "model.add(LSTM(32, dropout=0.2, recurrent_dropout=0.2, return_sequences=True))\n",
    "model.add(LSTM(32, dropout=0.2, recurrent_dropout=0.2))\n",
    "model.add(Dense(1, activation='sigmoid'))\n",
    "\n",
    "# Compile the model\n",
    "model.compile(optimizer='rmsprop', loss='binary_crossentropy', metrics=['acc'])\n",
    "\n",
    "# Define early stopping and learning rate reduction callbacks\n",
    "early_stopping = EarlyStopping(monitor='val_loss', patience=3, verbose=1, mode='min')\n",
    "reduce_lr = ReduceLROnPlateau(monitor='val_loss', factor=0.1, patience=2, verbose=1, mode='min')\n",
    "\n",
    "# Train the model\n",
    "history = model.fit(\n",
    "    x_train, y_train ,\n",
    "    batch_size=128,\n",
    "    epochs=15,\n",
    "    validation_data=(x_test, y_test),\n",
    "    callbacks=[early_stopping, reduce_lr]\n",
    ")\n",
    "#save the model in pickle format\n",
    "pickle.dump(model, open('model.pkl', 'wb'))\n",
    "#save the tokenizer in pickle format\n",
    "pickle.dump(tokenizer, open('tokenizer.pkl', 'wb'))\n",
    "\n",
    "\n",
    "# Evaluate the model on the test set\n",
    "score, acc = model.evaluate(x_test, y_test, batch_size=128)\n",
    "print('Test score:', score)\n",
    "print('Test accuracy:', acc)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7885e840",
   "metadata": {},
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  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "43521b94",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "782/782 [==============================] - 11s 14ms/step\n",
      "Saved results to CSV file.\n"
     ]
    }
   ],
   "source": [
    "# Get predictions on the test set\n",
    "# Get predicted probabilities on the test set\n",
    "y_pred_prob = model.predict(x_test)\n",
    "\n",
    "# Convert probabilities to classes\n",
    "y_pred = np.argmax(y_pred_prob, axis=1)\n",
    "\n",
    "# Convert the integer labels to sentiment strings\n",
    "sentiments = ['negative', 'positive']\n",
    "y_test_str = np.array([sentiments[label] for label in y_test])\n",
    "y_pred_str = np.array([sentiments[label] for label in y_pred])\n",
    "\n",
    "# Store the results in a CSV file\n",
    "results = pd.DataFrame({'Review': x_test_str, 'Actual Sentiment': y_test_str, 'Predicted Sentiment': y_pred_str})\n",
    "results.to_csv('imdb_sentiments.csv', index=False)\n",
    "\n",
    "print('Saved results to CSV file.')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "92a7b5ad",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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