Authors : Rohit Kumar Singh; Shivendra Pratap Singh; Abhinav Gupta; Prabal Bhatnagar

Volume/Issue : Volume 9 - 2024, Issue 9 - September

Google Scholar : https://tinyurl.com/za9wfwwv

Scribd : https://tinyurl.com/bdhfa7sx

DOI : https://doi.org/10.38124/ijisrt/IJISRT24SEP1396

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : In the contemporary digital era, online consumer evaluations exert significant sway over purchase choices, shaping consumer viewpoints and affecting business profitability. Nonetheless, the rise of counterfeit reviews has emerged as a notable apprehension, prompting scholars to investigate various methodologies for identification. This extensive review paper acts as a reservoir of information, consolidating an extensive array of literature dedicated to detecting fake reviews. It meticulously scrutinizes diverse datasets, illuminating the numerous hurdles posed by these misleading entries. Despite progress made in curtailing the impact of counterfeit reviews, this review exposes persisting gaps in our comprehension. Consequently, it calls for steadfast exploration and ingenuity in the realm of fake review detection. As the digital landscape continues to evolve, so too must our approaches to safeguard the authenticity of online consumer input.

Keywords : Fake Review Detection, Sentiment Analysis, Machine Learning Algorithms,Deep Learning Methods.

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