Exploiting Product Related Review Features for Fake Review Detection

Abstract

Product reviews are now widely used by individuals for making their decisions. However, due to the purpose of profit, reviewers game the system by posting fake reviews for promoting or demoting the target products. In the past few years, fake review detection has attracted significant attention from both the industrial organizations and academic communities. However, the issue remains to be a challenging problem due to lacking of labelling materials for supervised learning and evaluation. Current works made many attempts to address this problem from the angles of reviewer and review. However, there has been little discussion about the product related review features which is the main focus of our method. This paper proposes a novel convolutional neural network model to integrate the product related review features through a product word composition model. To reduce overfitting and high variance, a bagging model is introduced to bag the neural network model with two efficient classifiers. Experiments on the real-life Amazon review dataset demonstrate the effectiveness of the proposed approach.