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Detection of Fake Reviews on Online Products Using Machine Learning Algorithms

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Innovations in Bio-Inspired Computing and Applications (IBICA 2021)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 419))

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Abstract

Internet reviews may affect a customer’s selection, and they can assess a product by correlating it to other brands. If the reviews are authentic, the customer may only pick a product that fulfils their demands. If the reviews, on the other hand, are phoney, the buyer is duped. It is vital to obtain the identification of fake consumer opinions in order to address this issue. To evaluate if a review is fraudulent or not, the actions of reviewers are extracted based on a semantic analysis of his review content. In this study, a data set for a mixed product was retrieved from the web, along with reviews and other information about the reviewers, to identify false reviewers using four algorithms: Support Vector Machine, Logistic Regression, K-Nearest Neighbour and Decision Tree. The accuracy rate along with precision rate of the aforementioned four methods are used to validate the significance of the features on the choice. Experiments were carried out on a large number of reviews gathered from the internet, demonstrating the efficacy of the proposed method.

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Correspondence to H. Muthu Krishnan or J. Preetha .

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Krishnan, H.M., Preetha, J., Shona, S.P., Sivakami, A. (2022). Detection of Fake Reviews on Online Products Using Machine Learning Algorithms. In: Abraham, A., et al. Innovations in Bio-Inspired Computing and Applications. IBICA 2021. Lecture Notes in Networks and Systems, vol 419. Springer, Cham. https://doi.org/10.1007/978-3-030-96299-9_31

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