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; (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
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