i-manager Publications

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

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

Abstract

Keywords

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