CommTrust: Computing Multi-Dimensional Trust by Mining E-Commerce Feedback Comments

• Published in: IEEE transactions on knowledge and data engineering Vol. 26; no. 7; pp. 1631 - 1643
• Main Authors: Zhang, Xiuzhen, Cui, Lishan, Wang, Yan
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.07.2014; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithm design and analysis; Analytical models; Clustering algorithms; Computational modeling; Data mining; Educational institutions; Electronic Commerce; Feedback; Motion pictures; Reputations; Text mining; Trust; Electronic commerce; text mining
• ISSN: 1041-4347; 1558-2191
• DOI: 10.1109/TKDE.2013.177

Reputation-based trust models are widely used in e-commerce applications, and feedback ratings are aggregated to compute sellers' reputation trust scores. The "all good reputation" problem, however, is prevalent in current reputation systems-reputation scores are universally high for sellers and it is difficult for potential buyers to select trustworthy sellers. In this paper, based on the observation that buyers often express opinions openly in free text feedback comments, we propose CommTrust for trust evaluation by mining feedback comments. Our main contributions include: 1) we propose a multidimensional trust model for computing reputation scores from user feedback comments; and 2) we propose an algorithm for mining feedback comments for dimension ratings and weights, combining techniques of natural language processing, opinion mining, and topic modeling. Extensive experiments on eBay and Amazon data demonstrate that CommTrust can effectively address the "all good reputation" issue and rank sellers effectively. To the best of our knowledge, our research is the first piece of work on trust evaluation by mining feedback comments.