A Web-Based Sentiment Analysis System for IMDb Movie Reviews Using TF-IDF and Multinomial Naïve Bayes

Syahrur Ramadhan; Sadr Lufti Mufreni

doi:10.61255/decoding.v4i2.1385

Authors

Syahrur Ramadhan Universitas ‘Aisyiyah Yogyakarta
Sadr Lufti Mufreni Universitas ‘Aisyiyah Yogyakarta

DOI:

https://doi.org/10.61255/decoding.v4i2.1385

Keywords:

Multinomial naïve bayes, Natural language processing, Sentiment analysis, TF-IDF, Web application deployment

Abstract

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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References

A. N. Zhafira, N. Afifah, S. A. Putri, V. Marhalatun, D. Intan, and S. Saputra, “Analisis sentimen ulasan film pada IMDb menggunakan algoritma Naive Bayes,” JUMISTIK, vol. 4, no. 1, pp. 373–383, 2025, doi: 10.70247/jumistik.v4i1.139.

M. S. Islam et al., “Challenges and future in deep learning for sentiment analysis: a comprehensive review and a proposed novel hybrid approach,” Artif. Intell. Rev., vol. 57, no. 3, pp. 1–79, 2024, doi: 10.1007/s10462-023-10651-9.

N. S. Fathullah, Y. A. Sari, and P. P. Adikara, “Analisis sentimen terhadap rating dan ulasan film dengan menggunakan metode klasifikasi Naïve Bayes dengan fitur lexicon-based,” J. Pengemb. Teknol. Inf. dan Ilmu Komput., vol. 4, no. 2, pp. 590–593, 2020.

N. Malik and M. Bilal, “Natural language processing for analyzing online customer reviews: a survey, taxonomy, and open research challenges,” PeerJ Comput. Sci., vol. 10, p. e2203, 2024, doi: 10.7717/peerj-cs.2203.

K. P. Harmandini and K. Muslim, “Analysis of TF-IDF and TF-RF feature extraction on product review sentiment,” Sink. J. dan Penelit. Tek. Inform., vol. 8, no. 2, pp. 929–937, 2024, doi: 10.33395/v8i2.13376.

Ismaturrahmi, M. Khadafi, and Amirullah, “Implementation of web-based sentiment analysis application on movie reviews using the Naïve Bayes algorithm,” J. Informatics Eng. Softw. Appl., vol. 1, no. 1, pp. 129–138, 2025.

Martiti and C. Juliane, “Implementation of Naïve Bayes algorithm on sentiment analysis application,” Adv. Eng. Res., vol. 207, pp. 193–200, 2021.

J. Lu, H. Fan, and Y. Zhang, “Sentiment analysis of IMDb movie reviews based on LSTM,” J. Adv. Eng. Technol., vol. 2, no. 2, pp. 1–11, 2025, doi: 10.62177/jaet.v2i2.429.

A. Gupta, S. Pandey, M. P. Behera, S. Darshana, and A. Dash, “Sentiment analysis of movie review using machine learning,” Educ. Adm. Theory Pract., vol. 29, no. 4, pp. 1724–1734, 2024, doi: 10.53555/kuey.v29i4.6616.

S. V. Kanse, O. A. Desai, and U. R. Pol, “Sentiment analysis of IMDb movie review using deep learning,” Int. Res. J. Mod. Eng. Technol. Sci., vol. 7, no. 4, pp. 1033–1039, 2025.

Suwarno, Wesly, and B. Syahputra, “Genre-based sentiment and emotion system for audience insight,” TIERS Inf. Technol. J., vol. 6, no. 2, pp. 217–240, 2025, doi: 10.38043/tiers.v6i2.7183.

A. Hermawan, R. Yusuf, B. Daniawan, and Junaedi, “Enhanced sentiment analysis and emotion detection in movie reviews using support vector machine algorithm,” TELKOMNIKA Telecommun. Comput. Electron. Control, vol. 23, no. 1, pp. 138–146, 2025, doi: 10.12928/TELKOMNIKA.v23i1.26377.

D. Subedi, N. Lamichhane, and N. Subedi, “Sentiment analysis of IMDb movie reviews using SVM and Naïve Bayes classifier,” J. Eng. Sci., vol. 4, no. 1, pp. 1–13, 2025, doi: 10.3126/jes2.v4i1.70138.

C. Dewi, R. Chen, H. J. Christanto, and F. Cauteruccio, “Multinomial Naïve Bayes classifier for sentiment analysis of Internet Movie Database,” Vietnam J. Comput. Sci., vol. 10, no. 4, pp. 485–498, 2023, doi: 10.1142/S2196888823500100.

M. M. Danyal, S. S. Khan, M. Khan, M. B. Ghaffar, B. Khan, and M. Arshad, “Sentiment analysis based on performance of Linear Support Vector Machine and Multinomial Naïve Bayes using movie reviews with baseline techniques,” J. Big Data, vol. 5, pp. 1–18, 2023, doi: 10.32604/jbd.2023.041319.

B. Paneru, B. Thapa, and B. Paneru, “Sentiment analysis of movie reviews: a Flask application using CNN with RoBERTa embeddings,” Syst. Soft Comput., vol. 7, p. 200192, 2025, doi: 10.1016/j.sasc.2025.200192.

Y. Mamani-Coaquira and E. Villanueva, “A review on text sentiment analysis with machine learning and deep learning techniques,” IEEE Access, vol. 12, pp. 193115–193130, 2024, doi: 10.1109/ACCESS.2024.3513321.

P. Monika, C. Kulkarni, H. N. Kumar, S. Shruthi, and V. Vani, “Machine learning approaches for sentiment analysis: a survey,” Int. J. Health Sci. (Qassim)., vol. 6, no. S4, pp. 1286–1300, 2022, doi: 10.53730/ijhs.v6nS4.6119.

H. Zhao, Z. Liu, X. Yao, and Q. Yang, “A machine learning-based sentiment analysis of online product reviews with a novel term weighting and feature selection approach,” Inf. Process. Manag., vol. 58, no. 5, p. 102656, 2021, doi: 10.1016/j.ipm.2021.102656.

F. J. Damanik and D. B. Setyohadi, “Analysis of public sentiment about COVID-19 in Indonesia on Twitter using Multinomial Naïve Bayes and Support Vector Machine,” IOP Conf. Ser. Earth Environ. Sci., vol. 704, no. 1, p. 012027, 2021, doi: 10.1088/1755-1315/704/1/012027.

T. M. A. Admira, Sutarno, and M. Saefudin, “Model sentiment analysis berbasis machine learning untuk data Genz-Career Aspiration menggunakan Flask dan Naïve Bayes,” J. Inf. Syst. Informatics Comput., vol. 9, no. 1, pp. 25–39, 2025, doi: 10.52362/jisicom.v9i1.1880.

R. S. Pressman and B. R. Maxim, Software Engineering: A Practitioner’s Approach, 9th ed. New York, NY: McGraw-Hill Education, 2019.