Smart Detection of Review Fraud on E-Commerce Sites

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Ayush Rathore; Yashdeep Sahu; Sourabh Pandey; Kavyashree G. M

Smart Detection of Review Fraud on E-Commerce Sites

Authors: Ayush Rathore, Yashdeep Sahu, Sourabh Pandey, Kavyashree G. M

Unique Paper ID: 178600
Volume: 11
Issue: 12
PageNo: 4096-4098

Keywords: Machine Learning NLP XGBoost Logistic Regression Decision Tree Review Authenticity Random Forest

Abstract:
The surge of e-commerce has led to an increase in user-generated content, particularly reviews, many of which are fabricated by companies to boost product visibility and sales. These reviews, often posted by bots or paid individuals, undermine trust in digital marketplaces. This research introduces a comprehensive approach that integrates machine learning (ML) and natural language processing (NLP) to detect such deceptive reviews. Utilizing extensive preprocessing and advanced feature extraction techniques, our system effectively identifies subtle textual patterns and sentiments that differentiate genuine content from fraudulent ones. Both supervised and unsupervised ML models, including ensemble methods, were evaluated. Real-world datasets demonstrated the framework's ability to achieve high detection accuracy while minimizing false positives, thus contributing to the integrity and reliability of online shopping platforms.

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Cite This Article

ISSN: 2349-6002
Volume: 11
Issue: 12
PageNo: 4096-4098

Smart Detection of Review Fraud on E-Commerce Sites

Available:https://ijirt.org/article?manuscript=178600

Impact Factor
8.01 (Year 2024)

UGC Approved
Journal no 47859

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