Identifying Deceptive Reviews Using Machine Learning

Benson Mansingh, J. Sandeep, A. Basanth, M. Yagnesh, G. Asritha

2025

Abstract

Deceptive Reviews System that utilizes Machine Learning, natural language processing (NLP), and sentiment analysis to accurately distinguish between genuine and fraudulent reviews. The system enhances transparency and reliability in e- commerce by identifying deceptive feedback. It incorporates TF- IDF vectorization to extract key textual features. It supports informed purchasing decisions and helps businesses improve based on genuine user reviews, addressing the challenges posed by fake reviews in the digital marketplace. This solution plays a crucial role in maintaining the credibility and effectiveness of online review systems credibility and effectiveness of online review systems.

Paper Citation

in Harvard Style

Mansingh B., Sandeep J., Basanth A., Yagnesh M. and Asritha G. (2025). Identifying Deceptive Reviews Using Machine Learning. In Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies - ICRDICCT`25; ISBN 978-989-758-777-1, SciTePress, pages 789-794. DOI: 10.5220/0013890000004919

in Bibtex Style

@conference{icrdicct`2525,
author={Benson Mansingh and J. Sandeep and A. Basanth and M. Yagnesh and G. Asritha},
title={Identifying Deceptive Reviews Using Machine Learning},
booktitle={Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies - ICRDICCT`25},
year={2025},
pages={789-794},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013890000004919},
isbn={978-989-758-777-1},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies - ICRDICCT`25
TI - Identifying Deceptive Reviews Using Machine Learning
SN - 978-989-758-777-1
AU - Mansingh B.
AU - Sandeep J.
AU - Basanth A.
AU - Yagnesh M.
AU - Asritha G.
PY - 2025
SP - 789
EP - 794
DO - 10.5220/0013890000004919
PB - SciTePress