| Project Description: | Generally online (Electronic commerce or E-commerce) applications use reputation
reporting system for trust evaluation where they gather overall feedback ratings from the
sellers to compute the reputation score. A well-known issue with the reputation conduct
system is “all good reputation” problem where over 99% of feedback ratings are positive
leading to high reputation scores. This issue is hard on buyers to select accurate sellers.
By analyzing buyer’s opinions on free text feedback comments, we propose an approach
called the Reputation Analyzer. The main idea behind reputation analyzer is an algorithm
lexical-LDA (Latent Dirichlet Allocation) topic modeling technique proposed for mining
the online feedback comments by grouping aspect expressions into dimensions and
compute dimension ratings. Extensive experiments on eBay and Amazon data show that
the reputation analyzer can significantly solve the “all good reputation” problem and rank
sellers effectively. |
