ARCHIVES
Original Article
An Intelligent Sentiment Analysis System For Product Reviews Using Machine Learning
Vishnu Priya1
Devadarsha E2
Jothika R3
Mahalakshmi S4
Hemavathi K5
1 Assistant Professor, Department of Information Technology, Er. Perumal Manimekalai College of Engineering Hosur, Tamil Nadu, India. 2 3 4 5 B.Tech. Department of Information Technology, Er. Perumal Manimekalai College of Engineering Hosur, Tamil Nadu, India.
Published Online: March-April 2026
Pages: 220-226
Cite this article
↗ https://www.doi.org/10.59256/ijsreat.20260602027
References
1. B. Pang and L. Lee, "Opinion mining and sentiment analysis," Found. Trends Inf. Retr., vol. 2, no. 1-2, pp. 1–135, 2008.
2. B. Liu, "Sentiment analysis and opinion mining," Synth. Lect. Hum. Lang. Technol., vol. 5, no. 1, pp. 1–167, 2012.
3. J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova, "BERT: Pre-training of deep bidirectional transformers for language understanding,"
in Proc. of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language
Technologies, 2019, pp. 4171–4186.4. F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J.
Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and É. Duchesnay, "Scikit-learn: Machine learning in Python," J. Mach.
Learn. Res., vol. 12, pp. 2825–2830, 2011.
5. E. Loper and S. Bird, "NLTK: The natural language toolkit," in Proc. of the ACL 2002 Workshop on Effective Tools and Methodologies
for Teaching Natural Language Processing and Computational Linguistics, 2002, pp. 63–70.
6. McCallum and K. Nigam, "A comparison of event models for naive bayes text classification," in AAAI-98 Workshop on Learning for Text
Categorization, Madison, WI, 1998, pp. 41–48.
7. T. Mikolov, I. Sutskever, K. Chen, G. S. Corrado, and J. Dean, "Distributed representations of words and phrases and their
compositionality," in Advances in Neural Information Processing Systems, 2013, pp. 3111–3119.
8. J. Pennington, R. Socher, and C. D. Manning, "GloVe: Global vectors for word representation," in Proc. of the 2014 Conference on
Empirical Methods in Natural Language Processing, 2014, pp. 1532–1543.
9. S. Bird, E. Klein, and E. Loper, Natural Language Processing with Python: Analyzing Text with the Natural Language Toolkit. O'Reilly
Media, 2009.
10. A. Vaswani, N. Shazeer, P. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin, "Attention is all you need," in
Advances in Neural Information Processing Systems, 2017, pp. 5998–6008.
11. M. T. Ribeiro, S. Singh, and C. Guestrin, "Why should I trust you? Explaining the predictions of any classifier," in Proc. of the 22nd ACM
SIGKDD International Conference on Knowledge Discovery and Data Mining, 2016, pp. 1135–1144.
12. H. He and E. A. Garcia, "Learning from imbalanced data," IEEE Trans. Knowl. Data Eng., vol. 21, no. 9, pp. 1263–1284, 2009.
13. N. V. Chawla, K. W. Bowyer, L. O. Hall, and W. P. Kegelmeyer, "SMOTE: Synthetic minority over-sampling technique," J. Artif. Intell.
Res., vol. 16, pp. 321–357, 2002.
14. D. Sculley, G. Holt, D. Golovin, E. Davydov, T. Phillips, D. Ebner, V. Chaudhary, and M. Young, "Hidden technical debt in machine
learning systems," in Advances in Neural Information Processing Systems, 2015, pp. 2503–2511.
15. S. Hochreiter and J. Schmidhuber, "Long short-term memory," Neural Comput., vol. 9, no. 8, pp. 1735–1780, 1997
2. B. Liu, "Sentiment analysis and opinion mining," Synth. Lect. Hum. Lang. Technol., vol. 5, no. 1, pp. 1–167, 2012.
3. J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova, "BERT: Pre-training of deep bidirectional transformers for language understanding,"
in Proc. of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language
Technologies, 2019, pp. 4171–4186.4. F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J.
Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and É. Duchesnay, "Scikit-learn: Machine learning in Python," J. Mach.
Learn. Res., vol. 12, pp. 2825–2830, 2011.
5. E. Loper and S. Bird, "NLTK: The natural language toolkit," in Proc. of the ACL 2002 Workshop on Effective Tools and Methodologies
for Teaching Natural Language Processing and Computational Linguistics, 2002, pp. 63–70.
6. McCallum and K. Nigam, "A comparison of event models for naive bayes text classification," in AAAI-98 Workshop on Learning for Text
Categorization, Madison, WI, 1998, pp. 41–48.
7. T. Mikolov, I. Sutskever, K. Chen, G. S. Corrado, and J. Dean, "Distributed representations of words and phrases and their
compositionality," in Advances in Neural Information Processing Systems, 2013, pp. 3111–3119.
8. J. Pennington, R. Socher, and C. D. Manning, "GloVe: Global vectors for word representation," in Proc. of the 2014 Conference on
Empirical Methods in Natural Language Processing, 2014, pp. 1532–1543.
9. S. Bird, E. Klein, and E. Loper, Natural Language Processing with Python: Analyzing Text with the Natural Language Toolkit. O'Reilly
Media, 2009.
10. A. Vaswani, N. Shazeer, P. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin, "Attention is all you need," in
Advances in Neural Information Processing Systems, 2017, pp. 5998–6008.
11. M. T. Ribeiro, S. Singh, and C. Guestrin, "Why should I trust you? Explaining the predictions of any classifier," in Proc. of the 22nd ACM
SIGKDD International Conference on Knowledge Discovery and Data Mining, 2016, pp. 1135–1144.
12. H. He and E. A. Garcia, "Learning from imbalanced data," IEEE Trans. Knowl. Data Eng., vol. 21, no. 9, pp. 1263–1284, 2009.
13. N. V. Chawla, K. W. Bowyer, L. O. Hall, and W. P. Kegelmeyer, "SMOTE: Synthetic minority over-sampling technique," J. Artif. Intell.
Res., vol. 16, pp. 321–357, 2002.
14. D. Sculley, G. Holt, D. Golovin, E. Davydov, T. Phillips, D. Ebner, V. Chaudhary, and M. Young, "Hidden technical debt in machine
learning systems," in Advances in Neural Information Processing Systems, 2015, pp. 2503–2511.
15. S. Hochreiter and J. Schmidhuber, "Long short-term memory," Neural Comput., vol. 9, no. 8, pp. 1735–1780, 1997
Related Articles
2026
Fake Currency Detection Using Deep Learning
2026
Smart E-Commerce System with Dynamic Pricing
2026
Personal Expense Tracker with Currency Converter
2026
Paw Safe: An Extensive Technology-Driven Framework for Stray Dog Rescue, Healthcare Management, Community Engagement, and Smart Urban Governance
2026
Design and Development of a Full-Stack E-Commerce Website
2026
