Evaluating the Quality of Online Reviews based on Feature-richness

Authors

Kaiyu Liu, School of Information Management, Wuhan University, Wuhan, 430072, China; Center for E-commerce Research and Development, Wuhan University, Wuhan, 430072, China
Zhongyi Hu, School of Information Management, Wuhan University, Wuhan, 430072, China; Center for E-commerce Research and Development, Wuhan University, Wuhan, 430072, ChinaFollow
Xiao Huang, School of Information Management, Wuhan University, Wuhan, 430072, China; Center for E-commerce Research and Development, Wuhan University, Wuhan, 430072, China
Jiang Wu, School of Information Management, Wuhan University, Wuhan, 430072, China; Center for E-commerce Research and Development, Wuhan University, Wuhan, 430072, China

Abstract

Given the massive online reviews from online travel agencies, it is difficult for users to find high-quality reviews. Evaluating online reviews’ quality has been an important matter of concern. In this study, a review quality assessment model based on feature richness was proposed by combining grounded theory and semantic similarity. The proposed model can properly evaluate the quality of online reviews from the perspective of feature richness, and the more comprehensive the review content, the higher the quality is. Based on the online review from ctrip.com, experimental results showed that the proposed model can accurately identify the reviews that contain rich information with a high reference value for other users.

Recommended Citation

Liu, Kaiyu; Hu, Zhongyi; Huang, Xiao; and Wu, Jiang, "Evaluating the Quality of Online Reviews based on Feature-richness" (2022). WHICEB 2022 Proceedings. 3.
https://aisel.aisnet.org/whiceb2022/3