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Abstract

This study presents a comparative analysis of machine learning and deep learning algorithms for sentiment classification in movie reviews. Three benchmark datasets—IMDb (50K and 20K reviews) and Rotten Tomatoes—were used to evaluate six classifiers: Na¨ıve Bayes, Logistic Regression, Support Vector Machine, Decision Tree, Random Forest, and BERT. Preprocessing included tokenization, stop-word removal, stemming, and feature extraction using Count Vectorizer and TF-IDF. Evaluation metrics such as accuracy, precision, recall, sensitivity, specificity, and F1-score were used to assess model performance. Logistic Regression achieved 88% accuracy on the IMDb dataset, while Random Forest exhibited the highest specificity. BERT outperformed traditional models in both accuracy and F1-score across all datasets, particularly in handling informal and context-heavy language. The results highlight the impact of dataset characteristics on classification performance and provide insights for deploying sentiment analysis in real-world applications like recommendation systems and audience profiling.

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