FAKE REVIEW DETECTION SYSTEM USING MACHINE LEARNING AND NATURAL LANGUAGE PROCESSING

Abstract

Online platforms heavily rely on user reviews for decision-making, making the identification of fake reviews crucial. This paper presents a novel machine learning-based Fake Review Detection System that incorporates advanced linguistic and semantic analysis. The model's robustness is demonstrated through comprehensive evaluation metrics, showcasing its efficacy in real-world scenarios. Technological innovations in the backend system ensure seamless integration, scalability, and reliability. The outcome contributes to a deeper understanding of linguistic cues, providing a valuable tool for maintaining trust in online platforms. Numerous approaches are employed in detecting fake reviews, predominantly focusing on the linguistic cues of reviewers while overlooking their non-linguistic behaviors. This study identifies various non-linguistic behavioral traits of online reviewers and assesses their significance in detecting fake reviews compared to linguistic cues. Empirical findings from real-world online reviews demonstrate that integrating non-linguistic reviewer characteristics can substantially enhance the efficacy of fake review detection models.