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Research Article

A Deep Learning-Based Multilingual Product Review Sentiment Analysis System Using Distil BERT

  • ICCAIS-2026
  • Volume 5, Issue 1
  • Pages 44-49
  • Published Online: 2026
Open Access Peer Reviewed Crossref DOI

Authors

  • A. Pavan Sai Prasad1
  • GVN. Kishore2
  • G. Tanishq3
  • P. Chiranjeevi4
  • G. Vamshi Krishna5

1 3 4 5 Department of Artificial Intelligence and Machine Learning, Sasi Institute of Technology and Engineering Tadepalligudem, Andhra Pradesh, India. 2 Assistant Professor, Department of Artificial Intelligence and Machine Learning, Sasi Institute of Technology and Engineering, Tadepalligudem, Andhra Pradesh, India.

Article Information

Organised By
Madanapalle Institute of Technology & Science (MITS)
Conference Date
9 & 10 March 2026
Pages
44-49
Article Type
Research Article
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References

1. A.Kumar, R. Singh, and P. Verma, “EEBERT: An emoji-enhanced BERT fine tuning on Amazon product reviews for text sentiment classification,” IEEE Access, 2024. [Online]. Available: https://ieeexplore. ieee.org/abstract/document/10669019
2. S. Patel and M. Shah, “BERT-enhanced sentiment analysis for personalized e-commerce recommendations,” Multimedia Tools and Applications, Springer, 2023. [Online]. Available: https://link.springer.com/ article/10.1007/s11042-023-176895
3. R. Mehta, A. Gupta, and S. Nair, “Aspect-based sentiment classification of user reviews to understand customer satisfaction of e-commerce plat- forms,” Electronic Commerce Research, Springer, 2025. [Online]. Avail- able: https://link.springer.com/article/10.1007/s10660-025-09948-4
4. M. Hidayat, A. Nugroho, and D. Prasetyo, “Amazon products reviews classification based on machine learning, deep learning methods and BERT,” TELKOMNIKA (Telecommunication, Computing, Electronics and Control), vol. 21, no. 2, 2023. [Online]. Available: https://www.telkomnika.uad.ac.id/index.php/TELKOMNIKA/ article/view/24046/11728
5. S. Reddy and K. Rao, “A sentiment analysis of customer product review based on machine learning techniques in e-commerce,” ResearchGate, 2024. [Online]. Available: https://www.researchgate.net/ publication/384467289
6. P. Sharma and R. Malhotra, “Transformer-based sentiment analysis: A comparative study of machine learning, deep learning, and BERT models on Amazon product reviews,” Future Computing and Informatics Journal, 2023. [Online]. Available: https://www.macawpublications. com/Journals/index.php/FCR/article/view/102/122
7. J. Lee and H. Kim, “Enhancing product design through AI-driven sentiment analysis of Amazon reviews using BERT,” Algorithms, vol. 17, no. 2, MDPI, 2024. [Online]. Available: https://www.mdpi.com/ 1999-4893/17/2/59
8. L. Zhang, Y. Chen, and W. Li, “Sentiment analysis of emoji fused reviews using machine learning and BERT,” Scientific Reports, Nature Publishing Group, 2025. [Online]. Available: https://www.nature.com/ articles/s41598-025-92286-0
9. R. Iyer and S. Kulkarni, “Sentiment analysis in e-commerce platforms: A review of current techniques and future directions,” in Proceedings of the IEEE International Conference, 2023. [Online]. Available: https://ieeexplore.ieee.org/document/10225509
10. A.Banerjee and T. Das, “Product helpfulness detection with novel transformer-based BERT embedding and class probability features,” ResearchGate, 2024. [Online]. Available: https://www.researchgate.net/ publication/379692241
11. J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova, “BERT: Pre- training of deep bidirectional transformers for language understand- ing,” in Proceedings of NAACL-HLT, 2019. [Online]. Available: https://aclanthology.org/N19-1423/
12. V. Sanh, L. Debut, J. Chaumond, and T. Wolf, “DistilBERT, a distilled version of BERT: Smaller, faster, cheaper and lighter,” arXiv preprint arXiv:1910.01108, 2019. [Online]. Available: https://arxiv.org/abs/1910. 01108
13. Sun, X. Qiu, Y. Xu, and X. Huang, “How to fine-tune BERT for text classification?,” in Proceedings of the China National Conference on Chinese Computational Linguistics, 2019. [Online]. Available: https://arxiv.org/abs/1905.05583
14. S. Minaee, N. Kalchbrenner, E. Cambria, et al., “Deep learning-based text classification: A comprehensive review,” ACM Computing Surveys, vol. 54, no. 3, 2021. [Online]. Available: https://dl.acm.org/doi/10.1145/3439726
15. Z. Yang, Z. Dai, Y. Yang, J. Carbonell, R. Salakhutdinov, and Q.V. Le, “XLNet: Generalized autoregressive pretraining for language understanding,” in Advances in Neural Information Processing Systems (NeurIPS), 2019. [Online]. Available: https://papers.nips.cc/paper/2019/ hash/dc6a7e655d7e5840e66733e9ee67cc69-Abstract.html
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