An Online Fake Review Detection Approach Using Famous Machine Learning Algorithms

作     者：Asma Hassan Alshehri 

作者机构：Department of Computer SciencesCollege of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAl KharjSaudi Arabia 

出 版 物：《Computers, Materials & Continua》 (计算机、材料和连续体（英文）)

年 卷 期：2024年第78卷第2期

页      面：2767-2786页

核心收录：

学科分类：08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：Prince Sattam bin Abdulaziz University 

主　　题：Security fake review semi-supervised learning ML algorithms review detection 

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