An unsupervised approach to rank product reviews
Jianwei Wu, Bing Xu, Sheng Li · 2011
With the development of online shopping, more and more product reviews are acquired from online shopping sites, which vary a wide range in quality. In order to solve the problem of detecting low-quality reviews, we view the problem as a ranking task and a link analysis based ranking method is proposed. The proposed method requires no domain knowledge and no training data. Experiment results indicate that the proposed approach is effective in (1) showing comparable performance with the SVM (Support Vector Machines) regression method and (2) domain independent.