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

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, a survey on CommTrust for trust evaluation by mining feedback comments is done. Main contributions include 1) a multidimensional trust model for computing reputation scores from user feedback comments, and 2) 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.