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- from sklearn.datasets import load_files
- from sklearn.model_selection import train_test_split
- from sklearn.feature_extraction.text import TfidfVectorizer
- from sklearn.naive_bayes import MultinomialNB
- from sklearn.metrics import accuracy_score
- # Load the dataset
- movie_reviews_data = load_files('path_to_dataset', encoding='utf-8')
- # Split the dataset into training and testing sets
- X_train, X_test, y_train, y_test = train_test_split(
- movie_reviews_data.data, movie_reviews_data.target, test_size=0.2, random_state=42)
- # Vectorize the text data
- vectorizer = TfidfVectorizer(max_features=5000)
- X_train = vectorizer.fit_transform(X_train)
- X_test = vectorizer.transform(X_test)
- # Train the classifier
- classifier = MultinomialNB()
- classifier.fit(X_train, y_train)
- # Predict on the test set
- y_pred = classifier.predict(X_test)
- # Calculate accuracy
- accuracy = accuracy_score(y_test, y_pred)
- print("Accuracy:", accuracy)
- # Example usage
- def classify_review(review):
- review_vectorized = vectorizer.transform([review])
- prediction = classifier.predict(review_vectorized)
- if prediction[0] == 1:
- return "Positive"
- else:
- return "Negative"
- # Example usage
- review = "This movie was fantastic! I loved every moment of it."
- classification = classify_review(review)
- print("Review:", review)
- print("Classification:", classification)
- review = "The movie was terrible. I wouldn't recommend it to anyone."
- classification = classify_review(review)
- print("Review:", review)
- print("Classification:", classification)
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