Web services are commonly perceived as an environment of both offering opportunities and threats.In this environment,one way to minimize threats is to use reputation evaluation,which can be computed,for example,through transaction feedback.However,the current feedback-based approach is inaccurate and ineffective because of its inner limitations (e.g.,feedback quality problem).As the main source of feedback,the qualities of existing on-line reviews are often varied greatly from low to high,the main reasons include:(1) they have no standard expression formats,(2) dishonest comments may exist among these reviews due to malicious attacking.Up to present,the quality problem of review has not been well solved,which greatly degrades their importance on service reputation evaluation.Therefore,we firstly present a novel evaluation approach for review quality in terms of multiple metrics.Then,we make a further improvement in service reputation evaluation based on those filtered reviews.Experimental results show the effectiveness and efficiency of our proposed approach compared with the naive feedback-based approaches. Web services are commonly perceived as an environment of both offering opportunities and threatss.In this environment, one way to minimize threats is to use reputation evaluation, which can be computed, for example, through transaction feedback. However, the current feedback-based approach is inaccurate and ineffective because of its inner limitations (eg, feedback quality problem). As the main source of feedback, the qualities of existing on-line reviews are often varied greatly from low to high, the main reasons include: (1) they have no standard expression formats, (2) dishonest comments may exist among these reviews due to malicious attacking. Up to present, the quality problem of review has not been well solved, which greatly degrades their importance on service reputation evaluation.Therefore, we first present a novel evaluation approach for review quality in terms of multiple metrics. Chen, we make a further improvement in service reputation evaluation based on those filtered reviews.Experimen tal results show the effectiveness and efficiency of our proposed approach compared with the naive feedback-based approaches.