Learning to Represent Review with Tensor Decomposition for Spam Detection
Review spam detection is a key task in opinion mining. To accomplish this type of detection, previous work has focused mainly on effectively representing fake and non fake reviews with discriminative features, which are discovered or elaborately designed by experts or developers. This paper proposes a novel review spam detection method that learns the representation of reviews automatically instead of heavily relying on experts' knowledge in a data driven manner. More specifically, according to 11 relations (generated automatically from two basic patterns) between reviewers and products, we employ tensor decomposition to learn the embeddings of the reviewers and products in a vector space. We collect relations between any two entities (reviewers and products) which results in much useful and global information. We concatenate the review text, the embeddings of the reviewer and the reviewed product as the representation of a review. Based on such representations, the classifier could identify the opinion spam more precisely. Experimental results on an open Yelp dataset show that our method could effectively enhance the spam detection accuracy compared with the stateof the art methods.
EMNLP