Fake review identification and utility evaluation model using machine learning

被引：2
|
作者：
Choi, Wonil [1 ]
Nam, Kyungmin [1 ]
Park, Minwoo [1 ]
Yang, Seoyi [1 ]
Hwang, Sangyoon [1 ]
Oh, Hayoung [2 ]
机构：
[1] Sungkyunkwan Univ, Dept Business Adm, Seoul, South Korea
[2] Sungkyunkwan Univ, Coll Comp & Informat, Seoul, South Korea
来源：
FRONTIERS IN ARTIFICIAL INTELLIGENCE | 2023年 / 5卷
基金：
新加坡国家研究基金会;
关键词：
machine learning; fake review; fake review detection technique; e-commerce; useful reviews; SVC; logistic regression;
D O I：
10.3389/frai.2022.1064371
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Due to the structural growth of e-commerce platforms, the frequency of exchange of opinions and the number of online reviews of platform participants related to products are increasing. However, given the growth of fake reviews, the corresponding growth in the quality of online reviews seems to be slow, at best. The number of cases of harm to retailers and customers caused by malicious false reviews is steadily increasing every year. In this context, it is becoming difficult for users to determine useful reviews amid a flood of information. As a result, the intrinsic value of online reviews that reduce uncertainty in pre-purchase decisions is blurred, and e-commerce platforms are on the verge of losing credibility and traffic. Through this study, we intend to present solutions related to review filtering and classification by constructing a model for judging the authenticity and usefulness of online reviews using machine learning.
引用
收藏
页数：13
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