Accurate trust evaluation is crucial for the success of e-commerce systems. Reputation reporting systems have been implemented in e-commerce systems such as eBay and Amazon (for third-party sellers), where overall reputation scores for sellers are computed by aggregating feedback ratings. 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 abstract, 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; (2) We propose an algorithm for mining feedback comments for dimension ratings and weights, combining techniques of natural language processing, opinion mining and topic modelling. 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.
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