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Original Article

An Intelligent Sentiment Analysis System For Product Reviews Using Machine Learning

  • Paper ID 302
  • Volume 6, Issue 2
  • Pages 220-226
  • Published Online: March-April 2026
  • 7 Views
Open Access Peer Reviewed Crossref DOI

Authors

  • 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.

Article Information

Published Online
March-April 2026
Pages
220-226
Article Type
Original Article
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References

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compositionality," in Advances in Neural Information Processing Systems, 2013, pp. 3111–3119.
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Empirical Methods in Natural Language Processing, 2014, pp. 1532–1543.
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Media, 2009.
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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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