Optimized multi-class sentiment classification of Flipkart product reviews using deep learning
*Sonica UpadhyayCorresponding authors40847819@gmail.comDepartment of Computer ScienceBanasthali VidyapithNewai, Rajasthan, 304022, IndiaView full profile → , Yogesh Kumar Guptagyogesh@banasthali.inDepartment of Computer ScienceBanasthali VidyapithNewai, Rajasthan, 304022, IndiaView full profile →
* Corresponding author · click or hover a name for details
- Received:
- 01 Apr 2025
- Published Online:
- 23 Apr 2026
- Article type:
- Research Article
- Language:
- EN
- Article no.:
- JIOS-2291
- Pages:
- 2003–2010
Abstract
Keywords
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References
[1] A. Rohanroy, K. Rathor, and B. Jannawat, “Reviewing Flipkart product comments using methods based on sentiment analysis,” Int. Res. J. Modernization Eng., Technol. Sci., vol. 5, no. 2, pp. 800–806 (2023).
[2] Y. Uttam and D. Sandeep, “Aspect-based sentiment analysis on Flipkart data,” Int. J. Res. Appl. Sci., Eng. Technol., vol. 11, no. 5, pp. 5489–5503 (2023).
[3] A. Godia and L. K. Tiwari, “Sentiment analysis and classification of product reviews: A comprehensive study using NLP and machine learning techniques,” in Proc. 10th Int. Conf. Adv. Comput. Commun. Syst., pp. 1247–1252 (2024), doi: 10.1109/ICACCS60874.2024.10717296.
[4] K. S. Kumar, B. Bharathi, and K. Susmitha, “Advanced machine learning based aspect level sentiment analysis for Flipkart products,” Int. J. Creative Res. Thoughts, vol. 11, no. 4, pp. 348–352 (2023).
[5] M. S. Lakshmi, S. P. Kumar, and M. Janardhan, “Machine learning centric product endorsement on Flipkart database,” Int. J. Eng. Adv. Technol. (IJEAT), vol. 9, no. 1, pp. 1583–1586 (2019).
[6] V. Shukla and S. Bhoite, “Sentimental analysis of customer reviews by using data analysis,” in Proc. 3rd Natl. Level Students’ Res. Conf. Innovative Ideas Inventions Comput. Sci. IT Sustainability, vol. 8, no. 6, pp. 307–315 (2022).
[7] B. M. Munaf, R. Ansari, and S. O. A. Khan, “Flipkart reviews sentiment analysis using Python,” Int. J. Res. Publication Rev., vol. 5, no. 8, pp. 2841–2842 (2024).
[8] M. Birjali, M. Kasri, and A. Beni-Hssane, “A comprehensive survey on sentiment analysis: Approaches, challenges and trends,” Knowledge-Based Syst., vol. 226, p. 107134 (2021), doi: 10.1016/j.knosys.2021.107134.
[9] I. Raj, M. S. Jhala, and A. Patil, “Reviewing Flipkart product comments using methods based on sentiment analysis,” Int. J. Adv. Res. Sci. Commun. Technol., vol. 4, no. 5, pp. 644–648 (2022).
[10] S. Kamış and D. Goularas, “Evaluation of deep learning techniques in sentiment analysis from Twitter data,” in Proc. IEEE, pp. 12–17 (2019), doi: 10.1109/DeepML.2019.00011.
[11] A. Kumar, V. Tyagi, and S. Das, “Sentiment analysis on Twitter data using deep learning approach,” in Proc. IEEE, pp. 187–190 (2021), doi: 10.1109/ICACCCN51052.2020.9362853.
[12] M. G. Dhote, B. M. Nanche, P. N. Mahalle, S. S. Ali, and V. S. Karwande, “Deep learning for optimized path planning in autonomous vehicles by integrating reinforcement learning with convolutional neural networks,” J. Inf. Optim. Sci., vol. 46, no. 4B, pp. 1129–1139 (2025), doi: 10.47974/JIOS-1897.
[13] R. K. Moje, B. Tiple, S. M. Patil, and A. Revekart, “A framework formulti-task learning optimization in deep neural networks: Balancing task priorities for improved performance,” J. Inf. Optim. Sci., vol. 46, no. 4B, pp. 1129–1139 (2025).
[14] A. R. Deshmukh, P. S. Dhumal, and S. Bhattacharya, “Adaptive noise injection techniques for optimizing deep learning models under adversarial attacks,” J. Inf. Optim. Sci., vol. 46, no. 4B, pp. 1153–1163 (2025).




