Citation

Choi W, Nam K, Park M, Yang S, Hwang S, Oh H. Front. Artif. Intell. 2022; 5: e1064371.

Copyright

(Copyright © 2022, Frontiers Media)

DOI

10.3389/frai.2022.1064371

PMID

36744111

PMCID

PMC9893788

Abstract

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.

Language: en

Keywords

machine learning; e-commerce; fake review; fake review detection technique; logistic regression; SVC; useful reviews