Open Access

ARTICLE

An Online Fake Review Detection Approach Using Famous Machine Learning Algorithms

Asma Hassan Alshehri^*

Department of Computer Sciences, College of Computer Engineering and Sciences, Prince Sattam bin Abdulaziz University, Al Kharj, Saudi Arabia

* Corresponding Author: Asma Hassan Alshehri. Email:

Computers, Materials & Continua 2024, 78(2), 2767-2786. https://doi.org/10.32604/cmc.2023.046838

Received 16 October 2023; Accepted 18 December 2023; Issue published 27 February 2024

Abstract

Online review platforms are becoming increasingly popular, encouraging dishonest merchants and service providers to deceive customers by creating fake reviews for their goods or services. Using Sybil accounts, bot farms, and real account purchases, immoral actors demonize rivals and advertise their goods. Most academic and industry efforts have been aimed at detecting fake/fraudulent product or service evaluations for years. The primary hurdle to identifying fraudulent reviews is the lack of a reliable means to distinguish fraudulent reviews from real ones. This paper adopts a semi-supervised machine learning method to detect fake reviews on any website, among other things. Online reviews are classified using a semi-supervised approach (PU-learning) since there is a shortage of labeled data, and they are dynamic. Then, classification is performed using the machine learning techniques Support Vector Machine (SVM) and Nave Bayes. The performance of the suggested system has been compared with standard works, and experimental findings are assessed using several assessment metrics.

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Keywords

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