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- Vol. 6 Issue 2
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An Intelligent Sentiment Analysis System For Product Reviews Using Machine Learning
Authors
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.
Article Information
- Published In
- Volume 6, Issue 2 (2026)
- Published Online
- March-April 2026
- Pages
- 220-226
- Article Type
- Original Article
References
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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
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