Customer Sentiment Analysis and Insights Visualization for E-Commerce Using Machine Learning and Deep Learning Techniques

DOI

10.2991/978-94-6239-693-7_94How to use a DOI?

Keywords

Sentiment Analysis; E-commerce; Machine Learning; Deep Learning; BiLSTM; Text Classification

Abstract

Customer sentiment analysis will make us aware of their thoughts on online shopping websites and make more informed decisions. Due to the growth of online marketplaces, we require automatic tools to discover valuable insights when working with very large volumes of customer comments. In this paper we have sentimentally classified Amazon Fine Food Reviews records of data using standard machine learning and deep learning architectures, including logistic regression, Naïve Bayes, XGBoost, random forest, decision tree, single-layer LSTM, LSTM with a dropout layer, and bidirectional LSTM.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Download article (PDF)

TY  - CONF
AU  - Hemant Satwal
AU  - Ambily Balaram
AU  - M. Manikandakumar
PY  - 2026
DA  - 2026/06/16
TI  - Customer Sentiment Analysis and Insights Visualization for E-Commerce Using Machine Learning and Deep Learning Techniques
BT  - Proceedings of the International Conference on Intelligent Systems for a Sustainable Future (ISSF 2026)
PB  - Atlantis Press
SP  - 971
EP  - 979
SN  - 2589-4919
UR  - https://doi.org/10.2991/978-94-6239-693-7_94
DO  - 10.2991/978-94-6239-693-7_94
ID  - Satwal2026
ER  -