Comparative Study of Machine Learning Models for Sentiment Analysis of Amazon Product Reviews

Tri Noviantoro; Suryaneta Suryaneta

doi:10.55927/fjcis.v5i1.16389

Authors

Tri Noviantoro Universitas Muhammadiyah Lampung
Suryaneta Suryaneta Institut Teknologi Sumatera

DOI:

https://doi.org/10.55927/fjcis.v5i1.16389

Keywords:

Amazon Product Reviews, BERT, Customer Behavior, Machine Learning, Sentiment Analysis

Abstract

This research presents a comparative analysis of four popular sentiment classification models: Naive Bayes, Support Vector Machine (SVM), Long Short-Term Memory (LSTM) networks, and Bidirectional Encoder Representations from Transformers (BERT). The models are evaluated using the Amazon Product Reviews dataset based on their ability to classify sentiments into positive or negative categories. The results show that BERT outperforms the other models in accuracy, precision, recall, and F1-score, demonstrating its superior ability to capture complex contextual relationships in text. LSTM performed well, particularly in recalling positive sentiments, but was outperformed by BERT overall. Conversely, Naive Bayes and SVM exhibited lower accuracy and higher false positive rates, highlighting their limitations in handling nuanced, context-dependent text. This study emphasizes the trade-offs between traditional machine learning models and advanced deep learning techniques.

Downloads

Download data is not yet available.

References

Cheung, C. M. K., & Thadani, D. R. (2012). The impact of electronic word-of-mouth communication: A literature analysis and integrative model. Decision Support Systems, 54(1), 461–470. https://doi.org/10.1016/j.dss.2012.06.008.

Cortess, C., & Vapnik, V. (1995). Support vector network. Machine Learning, 20(3), 273–297. https://doi.org/https://doi.org/10.1023/A:1022627411411.

Devlin, J., Chang, M.-W., Lee, K., Google, K. T., & Language, A. I. (2018). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. Naacl-Hlt 2019, Mlm, 4171–4186. https://aclanthology.org/N19-1423.pdf.

Goldberg, Y. (2016). A primer on neural network models for natural language processing. Journal of Artificial Intelligence Research, 57, 345–420. https://doi.org/10.1613/jair.4992.

Kingma, D. P., & Ba, J. L. (2015). Adam: A Method for Stochastic Optimization. ICLR 2015, 1–15. https://arxiv.org/pdf/1412.6980.

Liu, B. (2020). Sentiment Analysis: Mining Opinions, Sentiments, and Emotions, Second Edition. In Sentiment Analysis: Mining Opinions, Sentiments, and Emotions, Second Edition (Issue May). https://doi.org/10.1017/9781108639286.

Liu, X., Shin, H., & Burns, A. C. (2021). Examining the impact of luxury brand’s social media marketing on customer engagement: Using big data analytics and natural language processing. Journal of Business Research, 125(April), 815–826. https://doi.org/10.1016/j.jbusres.2019.04.042.

Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., & Stoyanov, V. (2019). RoBERTa: A Robustly Optimized BERT Pretraining Approach. CoRR, abs/1907.1(1). http://arxiv.org/abs/1907.11692.

Mienye, I. D., Swart, T. G., & Obaido, G. (2024). Recurrent Neural Networks: A Comprehensive Review of Architectures, Variants, and Applications. Information, 15(9), 517. https://doi.org/10.3390/info15090517.

Mustak, M., Hallikainen, H., Laukkanen, T., Plé, L., Hollebeek, L. D., & Aleem, M. (2024). Using machine learning to develop customer insights from user-generated content. Journal of Retailing and Consumer Services, 81(August). https://doi.org/10.1016/j.jretconser.2024.104034.

Pennington, J., Socher, R., & ManningChristopher. (2014). GloVe: Global Vectors for Word Representation. Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), 1532–1543. https://doi.org/10.3115/v1/D14-1162.

Salcedo-Sanz, S., Rojo-Álvarez, J. L., Martínez-Ramón, M., & Camps-Valls, G. (2014). Support vector machines in engineering: An overview. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 4(3), 234–267. https://doi.org/10.1002/widm.1125.

Sebastiani, F. (2002). Machine Learning in Automated Text Categorization. ACM Computing Surveys, 34(1), 1–47. https://doi.org/10.1145/505282.505283.

Shrestha, N., & Nasoz, F. (2019). Deep Learning Sentiment Analysis of Amazon.Com Reviews and Ratings. International Journal on Soft Computing, Artificial Intelligence and Applications, 8(1), 01–15. https://doi.org/10.5121/ijscai.2019.8101.

Sun, C., Qiu, X., Xu, Y., & Huang, X. (2019). How to Fine-Tune BERT for Text Classification? Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 11856 LNAI(2), 194–206. https://doi.org/10.1007/978-3-030-32381-3_16.

Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., & Polosukhin, I. (2017). Attention is all you need. Advances in Neural Information Processing Systems, 2017-Decem(Nips), 5999–6009. https://doi.org/https://doi.org/10.48550/arXiv.1706.03762.

Yadav, P., Kashyap, I., & Bhati, B. S. (2024). Contextual Ambiguity Framework for Enhanced Sentiment Analysis. Tehnicki Glasnik, 18(3), 385–393. https://doi.org/10.31803/tg-20231227064230.

Yu, Y., Si, X., Hu, C., & Jianxun, Z. (2019). A Review of Recurrent Neural Networks: LSTM Cells and Network Architectures. Neural Computation, 1–36. https://doi.org/10.1162/neco_a_01199.