sentiment_analysis_lstm.py

# Import necessary libraries
import numpy as np
from tensorflow.keras.datasets import imdb
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Embedding, LSTM, Dense, Dropout, Bidirectional
from tensorflow.keras.preprocessing.sequence import pad_sequences
import matplotlib.pyplot as plt

# Set the vocabulary size and maximum length of each review
vocab_size = 10000
max_length = 300

# Load the IMDB dataset
(X_train, y_train), (X_test, y_test) = imdb.load_data(num_words=vocab_size)

# Pad sequences to ensure uniform input size
X_train = pad_sequences(X_train, maxlen=max_length)
X_test = pad_sequences(X_test, maxlen=max_length)

# Build the LSTM model
model = Sequential()
model.add(Embedding(input_dim=vocab_size, output_dim=128, input_length=max_length))
model.add(Bidirectional(LSTM(64, return_sequences=True)))  # Bidirectional LSTM
model.add(Dropout(0.5))
model.add(LSTM(32))
model.add(Dense(1, activation='sigmoid'))  # Output layer for binary classification

# Compile the model
model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])

# Train the model
history = model.fit(X_train, y_train, epochs=5, batch_size=128, validation_split=0.2, verbose=2)

# Evaluate the model on the test set
test_loss, test_accuracy = model.evaluate(X_test, y_test, verbose=0)
print(f"Test Accuracy: {test_accuracy:.2f}")

# Plot the training and validation accuracy and loss
plt.figure(figsize=(12, 4))
plt.subplot(1, 2, 1)
plt.plot(history.history['accuracy'], label='Training Accuracy')
plt.plot(history.history['val_accuracy'], label='Validation Accuracy')
plt.title('Training and Validation Accuracy')
plt.xlabel('Epoch')
plt.ylabel('Accuracy')
plt.legend()

plt.subplot(1, 2, 2)
plt.plot(history.history['loss'], label='Training Loss')
plt.plot(history.history['val_loss'], label='Validation Loss')
plt.title('Training and Validation Loss')
plt.xlabel('Epoch')
plt.ylabel('Loss')
plt.legend()

plt.show()

# Predict sentiment for a new sample
new_review = "This movie was fantastic! I really enjoyed the story and the acting was superb."
# Preprocess the new review: tokenize and pad the sequence
from tensorflow.keras.preprocessing.text import Tokenizer
tokenizer = Tokenizer(num_words=vocab_size)
tokenizer.fit_on_texts([new_review])
new_review_sequence = tokenizer.texts_to_sequences([new_review])
new_review_padded = pad_sequences(new_review_sequence, maxlen=max_length)

# Predict the sentiment
prediction = model.predict(new_review_padded)
sentiment = "Positive" if prediction[0][0] > 0.5 else "Negative"
print(f"Predicted Sentiment: {sentiment}")