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Original Article
Advanced Recurrent Neural Network Driven Social Media Sentiment Analysis Using Instagram Reviews
NukathattuTejaswini1
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: March-April 2026
Pages: 144-152
Cite this article
↗ https://www.doi.org/10.59256/ijsreat.20260602021
References
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28. J. Howard and S. Ruder, “Universal Language Model Fine-tuning for Text Classification,” May 2018, [Online]. Available: http://arxiv.org/abs/1801.06146
29. D. W. Otter, J. R. Medina, and J. K. Kalita, “A Survey of the Usages of Deep Learning in Natural Language Processing,” Dec. 2019, [Online]. Available: http://arxiv.org/abs/1807.10854
30. S. Wu and M. Dredze, “Beto, Bentz, Becas: The Surprising Cross-Lingual Effectiveness of BERT,” Oct. 2019, [Online]. Available: http://arxiv.org/abs/1904.09077
31. M. Müller, M. Salathé, and P. E. Kummervold, “COVID-Twitter-BERT: A Natural Language Processing Model to Analyse COVID-19 Content on Twitter,” May 2020, [Online]. Available: http://arxiv.org/abs/2005.07503
32. R. Thoppilan et al., “LaMDA: Language Models for Dialog Applications,” Feb. 2022, [Online]. Available: http://arxiv.org/abs/2201.08239
2. G. Ravi Kumar, D. Sathvika, G. Dheeraj, and T. V. Raidu, “Sentiment Analysis on Instagram Using BiLSTM for Enhanced Social Media Opinion Mining.”
3. A. Purwarianti, I. A. Putu, and A. Crisdayanti, “Improving Bi-LSTM Performance for Indonesian Sentiment Analysis Using Paragraph Vector.”
4. M. Kayed, R. P. Díaz Redondo, A. Mabrouk, and M. Kayed, “Deep Learning-based Sentiment Classification: A Comparative Survey.”
5. L. Zhang, S. Wang, and B. Liu, “Deep Learning for Sentiment Analysis: A Survey.”
6. Y. Kim, “Convolutional Neural Networks for Sentence Classification,” Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing, Apr. 2014, doi: 10.3115/v1/D14-1181.
7. W. Zaremba, I. Sutskever, and O. Vinyals, “Recurrent Neural Network Regularization,” Feb. 2015, [Online]. Available: http://arxiv.org/abs/1409.2329
8. A. Graves, “Generating Sequences With Recurrent Neural Networks,” Jun. 2014, [Online]. Available: http://arxiv.org/abs/1308.0850
9. H. Sak, A. Senior, and F. Beaufays, “Long Short-Term Memory Based Recurrent Neural Network Architectures for Large Vocabulary Speech Recognition,” Feb. 2014, [Online]. Available: http://arxiv.org/abs/1402.1128
10. Pandiri Lavanya, Patinavalasa Durga Prasad, and Suneel Kumar Duvvuri, “Context-Aware Sentiment Classification of Movie Reviews Using Bidirectional LSTM Networks,” Int. J. Sci. Res. Sci. Eng. Technol., vol. 13, no. 2, pp. 159–171, Mar. 2026, doi: 10.32628/IJSRSET261371.
11. M. Schuster and K. K. Paliwal, “Bidirectional Recurrent Neural Networks,” 1997.
12. Z. Huang, W. Xu, and K. Yu, “Bidirectional LSTM-CRF Models for Sequence Tagging,” Apr. 2015.
13. S. Wu and M. Dredze, “Beto, Bentz, Becas: The Surprising Cross-Lingual Effectiveness of BERT,” Oct. 2019, [Online]. Available: http://arxiv.org/abs/1904.09077
14. T. Mikolov, I. Sutskever, K. Chen, G. Corrado, and J. Dean, “Distributed Representations of Words and Phrases and their Compositionality,” Oct. 2013, [Online]. Available: http://arxiv.org/abs/1310.4546
15. S. Mandt, M. D. Hoffman, and D. M. Blei, “Stochastic Gradient Descent as Approximate Bayesian Inference,” Jan. 2018, [Online]. Available: http://arxiv.org/abs/1704.04289
16. H.-T. Cheng et al., “Wide & Deep Learning for Recommender Systems,” Jun. 2016, [Online]. Available: http://arxiv.org/abs/1606.07792
17. Y. Qixuan, “Three-Class Text Sentiment Analysis Based on LSTM,” Dec. 2024, [Online]. Available: http://arxiv.org/abs/2412.17347
18. M. P. Mollah, “An LSTM model for Twitter Sentiment Analysis,” Dec. 2022, [Online]. Available: http://arxiv.org/abs/2212.01791
19. P. Liu, S. Joty, and H. Meng, “Fine-grained Opinion Mining with Recurrent Neural Networks and Word Embeddings,” Association for Computational Linguistics, 2015. [Online]. Available: https://github.com/ppfliu/opinion-target
20. S. Minaee, E. Azimi, and A. Abdolrashidi, “Deep-Sentiment: Sentiment Analysis Using Ensemble of CNN and Bi-LSTM Models,” Apr. 2019, [Online]. Available: http://arxiv.org/abs/1904.04206
21. M. Kamyab, G. Liu, and M. Adjeisah, “Attention-Based CNN and Bi-LSTM Model Based on TF-IDF and GloVe Word Embedding for Sentiment Analysis,” Applied Sciences (Switzerland), vol. 11, no. 23, Dec. 2021, doi: 10.3390/app112311255.
22. J. Pennington, R. Socher, and C. D. Manning, “GloVe: Global Vectors for Word Representation.” [Online]. Available: http://nlp.
23. A. Vaswani et al., “Attention Is All You Need,” 2023.
24. R. K. Das, M. Islam, M. M. Hasan, S. Razia, M. Hassan, and S. A. Khushbu, “Sentiment analysis in multilingual context: Comparative analysis of machine learning and hybrid deep learning models,” Heliyon, vol. 9, no. 9, Sep. 2023, doi: 10.1016/j.heliyon.2023.e20281.
25. D. Kingma and J. Ba, “Adam: A Method for Stochastic Optimization,” International Conference on Learning Representations, Apr. 2014.
26. T. Mikolov, K. Chen, G. Corrado, and J. Dean, “Efficient Estimation of Word Representations in Vector Space,” Sep. 2013, [Online]. Available: http://arxiv.org/abs/1301.3781
27. X. Zhang and Y. LeCun, “Text Understanding from Scratch,” Apr. 2016, [Online]. Available: http://arxiv.org/abs/1502.01710
28. J. Howard and S. Ruder, “Universal Language Model Fine-tuning for Text Classification,” May 2018, [Online]. Available: http://arxiv.org/abs/1801.06146
29. D. W. Otter, J. R. Medina, and J. K. Kalita, “A Survey of the Usages of Deep Learning in Natural Language Processing,” Dec. 2019, [Online]. Available: http://arxiv.org/abs/1807.10854
30. S. Wu and M. Dredze, “Beto, Bentz, Becas: The Surprising Cross-Lingual Effectiveness of BERT,” Oct. 2019, [Online]. Available: http://arxiv.org/abs/1904.09077
31. M. Müller, M. Salathé, and P. E. Kummervold, “COVID-Twitter-BERT: A Natural Language Processing Model to Analyse COVID-19 Content on Twitter,” May 2020, [Online]. Available: http://arxiv.org/abs/2005.07503
32. R. Thoppilan et al., “LaMDA: Language Models for Dialog Applications,” Feb. 2022, [Online]. Available: http://arxiv.org/abs/2201.08239
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