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@article{173829,
        author = {Prathamesh Pawar and Parinita J Chate and Aakriti Singh and Aditya Degaonkar and Sanskruti Patil},
        title = {A Hybrid NLP and ML Approach to Fake  Review Classification},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {10},
        pages = {1747-1750},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=173829},
        abstract = {This study focuses on the design and development of a Fake Review Detection System (FRDS) using concepts like Natural Language Processing (NLP) and Machine Learning (ML) to improve the credibility of online reviews. The aim of the system is to identify and filter fraudulent reviews by considering linguistic patterns, sentiment, and behavioural cues, ensuring trustworthiness for both consumers and businesses. By integrating IP tracking for geolocation analysis, the FRDS improves detection accuracy by correlating review origins with user activity. The model is trained on a variety of datasets to enhance its adaptability across different review platforms, incorporating continuous feedback to refine detection algorithms and stay aligned with emerging deceptive strategies [1].},
        keywords = {Fake Review Detection System (FRDS), Natural Language Processing (NLP), Machine Learning (ML), IP Tracking, Feedback Mechanism (FM).},
        month = {March},
        }