<![CDATA[Purpose – The rapid growth of online movie platforms has produced large volumes of user reviews that contain valuable audience opinions. However, manual review analysis is inefficient. This study aims to develop a web-based sentiment analysis application using TF-IDF feature representation and the Multinomial Naïve Bayes algorithm to classify movie reviews into positive and negative sentiments.Methods – The model was trained and evaluated using the IMDb 50K Movie Reviews dataset with an 80:20 train–test split. An additional 600 reviews from six different movies were used to demonstrate application-level implementation. Text preprocessing included cleaning, lowercase normalization, tokenization, stopword filtering, and lemmatization using Natural Language Processing techniques. The processed texts were transformed into TF-IDF vectors and classified using Multinomial Naïve Bayes with the default smoothing parameter (α = 1.0). The trained model was deployed in a Flask-based web application for interactive sentiment prediction.Findings – The model achieved an accuracy of 84.93%, with precision, recall, and F1-score showing relatively balanced performance across positive and negative classes. The web application successfully classified movie reviews and displayed sentiment distributions through an interactive interface.Research implications – The findings indicate that lightweight machine learning methods can support practical web-based sentiment analysis with low computational demands. However, performance may decline when processing sarcasm, irony, or implicit contextual meaning.Originality – This study combines benchmark evaluation with web-based validation using 600 additional real-world movie reviews, demonstrating practical applicability beyond dataset-level testing.]]>
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