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AI-Driven Detection of Group Shilling and Review Analysis Using Bisecting K-Means


Authors : M. Revathi; M. M. Harshitha; Dr. Girish Kumar D.

Volume/Issue : Volume 11 - 2026, Issue 4 - April

Google Scholar : https://tinyurl.com/5a8rtpps

Scribd : https://tinyurl.com/3bxyutm3

DOI : https://doi.org/10.38124/ijisrt/26apr680

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Abstract : Online customer reviews strongly affect purchasing decisions on e-commerce platforms. However, the increasing presence of fake and biased reviews created through coordinated group shilling activities reduces the reliability of recommendation systems. This project presents an AI-based review analysis system designed to detect fraudulent and suspicious review behavior using NLP and ML techniques. The system adopts a role-based framework with separate access for users and administrators. Users submit product reviews and ratings, while administrators analyze the collected data. Review text is processed using NLP techniques, and Bisecting K-Means clustering is applied to identify groups of similar review patterns and user behaviors. Suspicious clusters indicating coordinated manipulation are detected, thereby improving review credibility and recommendation accuracy.

Keywords : Artificial Intelligence, Natural Language Processing, Bisecting K-Means, Group Shilling Detection, Review Analysis, Machine Learning.

References :

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Online customer reviews strongly affect purchasing decisions on e-commerce platforms. However, the increasing presence of fake and biased reviews created through coordinated group shilling activities reduces the reliability of recommendation systems. This project presents an AI-based review analysis system designed to detect fraudulent and suspicious review behavior using NLP and ML techniques. The system adopts a role-based framework with separate access for users and administrators. Users submit product reviews and ratings, while administrators analyze the collected data. Review text is processed using NLP techniques, and Bisecting K-Means clustering is applied to identify groups of similar review patterns and user behaviors. Suspicious clusters indicating coordinated manipulation are detected, thereby improving review credibility and recommendation accuracy.

Keywords : Artificial Intelligence, Natural Language Processing, Bisecting K-Means, Group Shilling Detection, Review Analysis, Machine Learning.

Paper Submission Last Date
31 - May - 2026

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