ARCHIVES
Original Article
Balanced LSTM-Based Deep Learning Framework for Sentiment Analysis of Amazon Product Reviews
Penke Divya1
Suneel Kumar Duvvuri2
1 Student, Department of Computer Science, Government College (Autonomous), Rajahmundry, Andhra Pradesh, India. 2 Assistant Professor, Department of Computer Science, Government College (Autonomous), Rajahmundry, Andhra Pradesh, India.
Published Online: January-April 2026
Pages: 443-451
Cite this article
↗ https://www.doi.org/10.59256/indjcst.20260501049
References
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International Journal Of Recent Trends In Multidisciplinary Research, p. 96, Mar. 2026, doi: 10.59256/ijrtmr.20260602016.
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15. S. Bird, E. Klein, and E. Loper, Natural Language Processing with Python. O’Reilly Media, 2009. [Online]. Available:
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20. A. Vaswani et al., “Attention Is All You Need,” in Advances in Neural Information Processing Systems, 2017. [Online]. Available:
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[Online]. Available: https://www.jmlr.org/papers/v3/blei03a.html
24. K. Kowsari, K. Jafari Meimandi, M. Heidarysafa, S. Mendu, L. Barnes, and D. Brown, “Text Classification Algorithms: A Survey,”
Information, vol. 10, no. 4, p. 150, 2019, [Online]. Available: https://arxiv.org/abs/1904.08067
25. R. Johnson and T. Zhang, “Effective Use of Word Order for Text Categorization with Convolutional Neural Networks,” arXiv preprint
arXiv:1509.01626, 2015, [Online]. Available: https://arxiv.org/abs/1509.01626
26. J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova, “BERT: Pre-training of Deep Bidirectional Transformers for Language
Understanding,” arXiv preprint arXiv:1810.04805, 2018, [Online]. Available: https://arxiv.org/abs/1810.04805
27. A. Joulin, E. Grave, P. Bojanowski, and T. Mikolov, “Bag of Tricks for Efficient Text Classification,” arXiv preprint arXiv:1607.01759,
2017, [Online]. Available: https://arxiv.org/abs/1607.01759
28. D. P. Kadupu, D. P. Patinavalasa, and D. Suneel Kumar, “Deep Learning-Based Sentiment Analysis of Hotel Reviews Using LSTM and
Bidirectional LSTM Models,” Indian Journal of Computer Science and Technology, p. 262, Apr. 2026, doi:
10.59256/indjcst.20260501038.
29. S. Hochreiter and J. Schmidhuber, “Long Short-Term Memory,” Neural Comput., vol. 9, no. 8, pp. 1735–1780, 1997, [Online]. Available:
https://www.bioinf.jku.at/publications/older/2604.pdf
30. F. Chollet, Deep Learning with Python. Manning Publications, 2018. [Online]. Available: https://www.manning.com/books/deep-learning-
with-python
31. I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning. MIT Press, 2016. [Online]. Available: https://www.deeplearningbook.org
32. T. Brown et al., “Language Models are Few-Shot Learners,” in Advances in Neural Information Processing Systems (NeurIPS), 2020.
[Online]. Available: https://arxiv.org/abs/2005.14165
https://www.mckinsey.com/capabilities/quantumblack/our-insights/big-data-the-next-frontier-for-innovation
2. BrightLocal, “Local Consumer Review Survey 2023,” 2023. [Online]. Available: https://www.brightlocal.com/research/local-consumer-
review-survey/
3. C. D. Manning, P. Raghavan, and H. Schütze, Introduction to Information Retrieval. Cambridge University Press, 2008. [Online].
Available: https://nlp.stanford.edu/IR-book/
4. B. Liu, Sentiment Analysis and Opinion Mining. Morgan & Claypool, 2012. [Online]. Available:
https://www.cs.uic.edu/~liub/FBS/SentimentAnalysis-and-OpinionMining.pdf
5. B. Pang and L. Lee, “Opinion Mining and Sentiment Analysis,” Foundations and Trends in Information Retrieval, vol. 2, no. 1–2, pp. 1–
135, 2008, [Online]. Available: https://www.cs.cornell.edu/home/llee/omsa/omsa.pdf
6. H. He and E. A. Garcia, “Learning from Imbalanced Data,” IEEE Trans. Knowl. Data Eng., vol. 21, no. 9, pp. 1263–1284, 2009, [Online].Available: https://ieeexplore.ieee.org/document/4633969
7. E. Cambria, B. Schuller, Y. Xia, and C. Havasi, “Affective Computing and Sentiment Analysis,” IEEE Intell. Syst., vol. 32, no. 2, pp. 102–
107, 2017, [Online]. Available: https://ieeexplore.ieee.org/document/7888457
8. K. Kowsari, K. Jafari Meimandi, M. Heidarysafa, S. Mendu, L. Barnes, and D. Brown, “Text Classification Algorithms: A Survey,”
Information, vol. 10, no. 4, p. 150, 2019, [Online]. Available: https://arxiv.org/abs/1904.08067
9. Y. Kim, “Convolutional Neural Networks for Sentence Classification,” in EMNLP, 2014. [Online]. Available:
https://arxiv.org/abs/1408.5882
10. A. Vaswani et al., “Attention Is All You Need,” in NeurIPS, 2017. [Online]. Available: https://arxiv.org/abs/1706.03762
11. S. Hochreiter and J. Schmidhuber, “Long Short-Term Memory,” Neural Comput., vol. 9, no. 8, pp. 1735–1780, 1997, [Online]. Available:
https://www.bioinf.jku.at/publications/older/2604.pdf
12. D. P. Patinavalasa and D. Suneel Kumar, “Scalable Email Spam Detection Using BiLSTM with Large-Scale Hybrid Datasets,”
International Journal Of Recent Trends In Multidisciplinary Research, p. 96, Mar. 2026, doi: 10.59256/ijrtmr.20260602016.
13. N. Japkowicz, “The Class Imbalance Problem: Significance and Strategies,” International Conference on Artificial Intelligence, 2000,
[Online]. Available: https://link.springer.com/article/10.1023/A:1007649029923
14. S. K. DUVVURI, Applications of Artificial Intelligence Across Domains . Commissionerate of Collegiate Education, Government of
Andhra Pradesh , 2026. doi: 10.5281/zenodo.18623057.
15. S. Bird, E. Klein, and E. Loper, Natural Language Processing with Python. O’Reilly Media, 2009. [Online]. Available:
https://www.nltk.org/book/
16. J. Pennington, R. Socher, and C. Manning, “GloVe: Global Vectors for Word Representation,” in EMNLP, 2014, pp. 1532–1543. [Online].
Available: https://nlp.stanford.edu/projects/glove/
17. T. Fawcett, “An Introduction to ROC Analysis,” Pattern Recognit. Lett., vol. 27, no. 8, pp. 861–874, 2006, [Online]. Available:
https://dl.acm.org/doi/10.1145/1143844.1143874
18. X. Zhang, J. Zhao, and Y. LeCun, “Character-level Convolutional Networks for Text Classification,” Adv. Neural Inf. Process. Syst., 2015,
[Online]. Available: https://arxiv.org/abs/1509.01626
19. T. Young, D. Hazarika, S. Poria, and E. Cambria, “Recent Trends in Deep Learning Based Natural Language Processing,” IEEE Comput.
Intell. Mag., vol. 13, no. 3, pp. 55–75, 2018, [Online]. Available: https://ieeexplore.ieee.org/document/8416583
20. A. Vaswani et al., “Attention Is All You Need,” in Advances in Neural Information Processing Systems, 2017. [Online]. Available:
https://arxiv.org/abs/1706.03762
21. E. Cambria, B. Schuller, Y. Xia, and C. Havasi, “Affective Computing and Sentiment Analysis,” IEEE Intell. Syst., vol. 32, no. 2, pp. 102–
107, 2017, [Online]. Available: https://ieeexplore.ieee.org/document/7888457
22. T. Mikolov, K. Chen, G. Corrado, and J. Dean, “Efficient Estimation of Word Representations in Vector Space,” arXiv preprint
arXiv:1301.3781, 2013, [Online]. Available: https://arxiv.org/abs/1301.3781
23. D. M. Blei, A. Y. Ng, and M. I. Jordan, “Latent Dirichlet Allocation,” Journal of Machine Learning Research, vol. 3, pp. 993–1022, 2003,
[Online]. Available: https://www.jmlr.org/papers/v3/blei03a.html
24. K. Kowsari, K. Jafari Meimandi, M. Heidarysafa, S. Mendu, L. Barnes, and D. Brown, “Text Classification Algorithms: A Survey,”
Information, vol. 10, no. 4, p. 150, 2019, [Online]. Available: https://arxiv.org/abs/1904.08067
25. R. Johnson and T. Zhang, “Effective Use of Word Order for Text Categorization with Convolutional Neural Networks,” arXiv preprint
arXiv:1509.01626, 2015, [Online]. Available: https://arxiv.org/abs/1509.01626
26. J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova, “BERT: Pre-training of Deep Bidirectional Transformers for Language
Understanding,” arXiv preprint arXiv:1810.04805, 2018, [Online]. Available: https://arxiv.org/abs/1810.04805
27. A. Joulin, E. Grave, P. Bojanowski, and T. Mikolov, “Bag of Tricks for Efficient Text Classification,” arXiv preprint arXiv:1607.01759,
2017, [Online]. Available: https://arxiv.org/abs/1607.01759
28. D. P. Kadupu, D. P. Patinavalasa, and D. Suneel Kumar, “Deep Learning-Based Sentiment Analysis of Hotel Reviews Using LSTM and
Bidirectional LSTM Models,” Indian Journal of Computer Science and Technology, p. 262, Apr. 2026, doi:
10.59256/indjcst.20260501038.
29. S. Hochreiter and J. Schmidhuber, “Long Short-Term Memory,” Neural Comput., vol. 9, no. 8, pp. 1735–1780, 1997, [Online]. Available:
https://www.bioinf.jku.at/publications/older/2604.pdf
30. F. Chollet, Deep Learning with Python. Manning Publications, 2018. [Online]. Available: https://www.manning.com/books/deep-learning-
with-python
31. I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning. MIT Press, 2016. [Online]. Available: https://www.deeplearningbook.org
32. T. Brown et al., “Language Models are Few-Shot Learners,” in Advances in Neural Information Processing Systems (NeurIPS), 2020.
[Online]. Available: https://arxiv.org/abs/2005.14165
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