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@article{178600,
        author = {Ayush Rathore and Yashdeep Sahu and Sourabh Pandey and Kavyashree G. M},
        title = {Smart Detection of Review Fraud on E-Commerce Sites},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {12},
        pages = {4096-4098},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=178600},
        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.},
        keywords = {Machine Learning, NLP, XGBoost, Logistic Regression, Decision Tree, Review Authenticity, Random Forest},
        month = {May},
        }