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This study explores the application of machine learning techniques for sentiment analysis on movie reviews, focusing on accurately classifying user opinions as positive or negative. Using the IMDB movie review dataset, three machine learning algorithms Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Random Forest were evaluated to determine their effectiveness in sentiment classification. The dataset underwent preprocessing steps, including Bag-of-Words formatting and TF-IDF transformation, to optimize feature extraction. Performance was assessed using accuracy, precision, recall, and F1-score metrics through 5-fold cross-validation. The results showed that the SVM classifier achieved the highest accuracy (87.92%), followed by Random Forest (83.53%), while KNN lagged with 52.38%. The study highlights the superior performance of SVM in handling high-dimensional, sparse data, making it the most suitable model for sentiment analysis in movie reviews.

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Sentiment Analysis Using Machine Learning Technique

  • 会议论文

  • 发表日期：

    2026年

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  • Samuel-Soma M. Ajibade,
  • Muhammed Basheer Jasser,
  • Farrukh Hassan,
  • David Olayemi Alebiosu,
  • Anthonia Oluwatosin Adediran,
  • Kayode Akinlekan Akintoye,
  • Mbiatke Anthony Bassey

摘要

This study explores the application of machine learning techniques for sentiment analysis on movie reviews, focusing on accurately classifying user opinions as positive or negative. Using the IMDB movie review dataset, three machine learning algorithms Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Random Forest were evaluated to determine their effectiveness in sentiment classification. The dataset underwent preprocessing steps, including Bag-of-Words formatting and TF-IDF transformation, to optimize feature extraction. Performance was assessed using accuracy, precision, recall, and F1-score metrics through 5-fold cross-validation. The results showed that the SVM classifier achieved the highest accuracy (87.92%), followed by Random Forest (83.53%), while KNN lagged with 52.38%. The study highlights the superior performance of SVM in handling high-dimensional, sparse data, making it the most suitable model for sentiment analysis in movie reviews.