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| Content Provider | frontiers |
|---|---|
| Author | Choi, Wonil Nam, Kyungmin Park, Minwoo Yang, Seoyi Hwang, Sangyoon Oh, Hayoung |
| 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 becom-ing 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. |
| ISSN | 26248212 |
| DOI | 10.3389/frai.2022.1064371 |
| Volume Number | 5 |
| Journal | Frontiers in Artificial Intelligence |
| Language | English |
| Publisher Date | 2023-01-19 |
| Access Restriction | Open |
| Subject Keyword | Machine Learing E commerce Useful reviews Fake review detection technique Logistic regression Fake Review SVC |
| Content Type | Text |
| Resource Type | Article |
| Subject | Artificial Intelligence |
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