ReviewGuard: A Unified NLP Framework for Fake Review Detection, Emotion Classification, and Thematic Categorization Using LSTM-based Recurrent Neural Networks

DOI

10.2991/978-94-6463-866-0_80How to use a DOI?

Keywords

Fake review detection; NLP; LSTM; TF-IDF; Softmax; Multi-task learning; Emotion classification

Abstract

In the landscape of modern digital commerce, user- generated reviews are pivotal in shaping consumer preferences, influencing purchasing behavior, and contributing to brand perception. However, the infiltration of fraudulent or misleading reviews undermines the credibility of such platforms. This paper introduces ReviewGuard, a robust Natural Language Processing (NLP)-driven system designed to detect deceptive reviews, analyze sentiment, and classify thematic relevance. Our approach integrates classical machine learning models with deep learning techniques, specifically Long Short-Term Memory (LSTM) networks, to enhance classification accuracy. The system utilizes TF-IDF, semantic embeddings, and multi-task learning for concurrent fake review identification, emotion classification, and category tagging. A web-based interface enables real-time processing of review data. Evaluation on Amazon datasets demonstrates high precision, with the LSTM-based module achieving a classification accuracy of 95.4%. ReviewGuard offers a scalable solution for safeguarding e-commerce ecosystems against review manipulation and enhances user trust by ensuring transparency in feedback systems.

Copyright

© 2025 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.

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TY  - CONF
AU  - S. K. Hemanathan
AU  - Jeyshanth Leo
AU  - M. Hamshavardhan
PY  - 2025
DA  - 2025/10/31
TI  - ReviewGuard: A Unified NLP Framework for Fake Review Detection, Emotion Classification, and Thematic Categorization Using LSTM-based Recurrent Neural Networks
BT  - Proceedings of the International Conference on Intelligent Systems and Digital Transformation (ICISD 2025)
PB  - Atlantis Press
SP  - 992
EP  - 1003
SN  - 2589-4919
UR  - https://doi.org/10.2991/978-94-6463-866-0_80
DO  - 10.2991/978-94-6463-866-0_80
ID  - Hemanathan2025
ER  -