An Online Fake Review Detection Approach Using Famous Machine Learning Algorithms

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.