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Unveiling Sentiment Analysis: A Comparative Study of LSTM and Logistic Regression Models with XAI Insights

Chandu Vaidya *, Mayuri Botre**, Yash Rokde***, Sagar Kumbhalkar****, Soham Linge*****, Soham Pitale******, Shreyash Bawne*******

*-******* Department of Computer Science and Engineering, S.B. Jain Institute of Technology Management and Research, Nagpur, Maharashtra, India.

Periodicity:October - December'2023
DOI : https://doi.org/10.26634/jcom.11.3.20471

Abstract

In this study, we delve into sentiment analysis and the role of Explainable Artificial Intelligence (XAI), with a focus on techniques such as Lime that bring transparency to machine learning (Logistic Regression) and deep learning (LSTM) models. We explore how ML predictions can be biased using XAI and how XAI helps us understand DL models used in sentiment analysis through research that has been made. Examining various research, we notice a gap – the lack of training and interpretation for both ML and DL models on the same dataset using XAI. Our research fills this gap, shedding light on ML and DL model predictions through XAI's lens. By completing our research work, we come to know that even with an accuracy level of 83% for the DL model, they outperform the ML model with an accuracy level of 92% in some cases. This distinction is only identified with XAI techniques, particularly Lime.

Keywords

XAI (Explainable Artificial Intelligence), LIME (Local Interpretable Model Agnostic Explanations), Sentiment Analysis, Machine Learning, Deep Learning.

How to Cite this Article?

Vaidya, C. D., Botre, M., Rokde, Y., Kumbhalkar, S., Linge, S., Pitale, S., and Bawne, S. (2023). Unveiling Sentiment Analysis: A Comparative Study of LSTM and Logistic Regression Models with XAI Insights. i-manager’s Journal on Computer Science, 11(3), 36-46. https://doi.org/10.26634/jcom.11.3.20471

References

[1]. Adak, A., Pradhan, B., Shukla, N., & Alamri, A. (2022). Unboxing deep learning model of food delivery service reviews using explainable artificial intelligence (XAI) technique. Foods, 11(14), 2019.

[2]. Aqlan, A. A. Q., Manjula, B., & Naik, R. L. (2019). A study of sentiment analysis: Concepts, techniques, and challenges. In Proceedings of International Conference on Computational Intelligence and Data Engineering: Proceedings of ICCIDE 2018 (pp. 147-162). Springer Singapore.

[3]. Arras, L., Montavon, G., Müller, K. R., & Samek, W. (2017). Explaining recurrent neural network predictions in sentiment analysis. arXiv preprint arXiv:1706.07206.

[4]. Athar, A. (2014). Sentiment analysis of scientific citations. University of Cambridge, Computer Laboratory (pp. 1-114).

[5]. Dieber, J., & Kirrane, S. (2020). Why model why? Assessing the strengths and limitations of LIME. arXiv preprint arXiv:2012.00093.

[6]. Goebel, R., Chander, A., Holzinger, K., Lecue, F., Akata, Z., Stumpf, S., ... & Holzinger, A. (2018). Explainable AI: The new 42?. In International Cross-Domain Conference for Machine Learning and Knowledge Extraction (pp. 295-303). Springer, Cham.

[7]. Gohel, P., Singh, P., & Mohanty, M. (2021). Explainable AI: current status and future directions. arXiv preprint arXiv:2107.07045.

[8]. Hagras, H. (2018). Toward human-understandable, explainable AI. Computer, 51(9), 28-36.

[9]. Hoffman, R. R., Mueller, S. T., Klein, G., & Litman, J. (2018). Metrics for explainable AI: Challenges and prospects. arXiv preprint arXiv:1812.04608.

[10]. Jagdale, R. S., Shirsat, V. S., & Deshmukh, S. N. (2019). Sentiment analysis on product reviews using machine learning techniques. In Cognitive Informatics and Soft Computing: Proceeding of CISC 2017 (pp. 639- 647). Springer Singapore.

[11]. Love, P. E., Fang, W., Matthews, J., Porter, S., Luo, H., & Ding, L. (2023). Explainable artificial intelligence (XAI): Precepts, models, and opportunities for research in construction. Advanced Engineering Informatics, 57, 102024.

[12]. Lundberg, S. M., & Lee, S. I. (2017). A unified approach to interpreting model predictions. Advances in Neural Information Processing Systems, 30, 1-10.

[13]. Mathew, L., & Bindu, V. R. (2020, March). A review of natural language processing techniques for sentiment analysis using pre-trained models. In 2020 Fourth International Conference on Computing Methodologies and Communication (ICCMC) (pp. 340-345). IEEE.

[14]. McAuley, J. J., & Leskovec, J. (2013, May). From amateurs to connoisseurs: Modeling the evolution of user expertise through online reviews. In Proceedings of the 22nd International Conference on World Wide Web (pp. 897-908).

[15]. Nasukawa, T., & Yi, J. (2003, October). Sentiment analysis: Capturing favorability using natural language processing. In Proceedings of the 2nd International Conference on Knowledge Capture (pp. 70-77).

[16]. Pang, B., Lee, L., & Vaithyanathan, S. (2002). Thumbs up? Sentiment classification using machine learning techniques. arXiv preprint cs/0205070.

[17]. Raza, H., Faizan, M., Hamza, A., Mushtaq, A., & Akhtar, N. (2019). Scientific text sentiment analysis using machine learning techniques. International Journal of Advanced Computer Science and Applications, 10(12), 157-165.

[18]. Ribeiro, M. T., Singh, S., & Guestrin, C. (2016, August). "Why should i trust you?" Explaining the predictions of any nd classifier. In Proceedings of the 22 ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 1135-1144).

[19]. Shivaprasad, T. K., & Shetty, J. (2017, March). Sentiment analysis of product reviews: A review. In 2017 International Conference on Inventive Communication and Computational Technologies (ICICCT) (pp. 298-301). IEEE.

[20]. Silva, C., & Ribeiro, B. (2003, July). The importance of stop word removal on recall values in text categorization. In Proceedings of the International Joint Conference on Neural Networks, 2003, 3,1661-1666. IEEE.

[21]. So, C. (2020). What emotions make one or five stars? Understanding ratings of online product reviews by sentiment analysis and XAI. In Artificial Intelligence in HCI: First International Conference, AI-HCI 2020 (pp. 412-421). Springer International Publishing.

[22]. Sperandei, S. (2014). Understanding logistic regression analysis. Biochemia Medica, 24(1), 12-18.

[23]. Staudemeyer, R. C., & Morris, E. R. (2019). Understanding LSTM--a tutorial into long short-term memory recurrent neural networks. arXiv preprint arXiv:1909.09586.

[24]. Sun, F., Belatreche, A., Coleman, S., McGinnity, T. M., & Li, Y. (2014, March). Pre-processing online financial text for sentiment classification: A natural language processing approach. In 2014 IEEE Conference on Computational Intelligence for Financial Engineering & Economics (CIFEr) (pp. 122-129). IEEE.

[25]. Tyagi, A., & Sharma, N. (2018). Sentiment analysis using logistic regression and effective word score heuristic. International Journal of Engineering and Technology (UAE), 7(2.24), 20-23.

[26]. Vaidya, C., Gupta, A., Shastrakar, A., Kathane, A., Atmande, K., & Iyer, K. (2023, February). Sarcasm detection analysis–Comparative analysis. In 2023 IEEE International Students' Conference on Electrical, Electronics and Computer Science (SCEECS) (pp. 1-4). IEEE.

[27]. Wang, J., Yu, L. C., Lai, K. R., & Zhang, X. (2016, August). Dimensional sentiment analysis using a regional CNN-LSTM model. In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, 2, 225-230.

[28]. William, P., Shrivastava, A., Chauhan, P. S., Raja, M., Ojha, S. B., & Kumar, K. (2023). Natural Language processing implementation for sentiment analysis on tweets. In Mobile Radio Communications and 5G Networks: Proceedings of Third MRCN 2022 (pp. 317- 327). Springer Nature Singapore.

[29]. Xia, R., Zong, C., & Li, S. (2011). Ensemble of feature sets and classification algorithms for sentiment classification. Information Sciences, 181(6), 1138-1152.

[30]. Xu, F., Uszkoreit, H., Du, Y., Fan, W., Zhao, D., & Zhu, J. (2019). Explainable AI: A brief survey on history, research areas, approaches and challenges. In Natural Language Processing and Chinese Computing: 8th CCF International Conference, NLPCC 2019 (pp. 563-574). Springer International Publishing.

[31]. Yadav, A., & Vishwakarma, D. K. (2020). Sentiment analysis using deep learning architectures: A review. Artificial Intelligence Review, 53(6), 4335-4385.

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