A hierarchical neural network model with user and product attention for deceptive reviews detection

Abstract

Deceptive reviews detection has attracted extensive attentions from the business and research communities in recent years. Existing work mainly uses traditional discrete models with rich features from the viewpoint of linguistics and psycholinguistics. The drawback is that these models fail to capture the global semantic information of a sentence or discourse. Recently, neural network models provide new solutions for this task, and can be used to learn global representation of a review text, achieving competitive performance. We observe that a review text usually contains two types of information. Some words or sentences describe the user’s preferences, while others indicate the characteristics of the product. Based on this observation, this paper explores a hierarchical neural network model with attention mechanism, which can learn a global review representation from the viewpoint of user and product, to identify deceptive reviews. Experimental results show that the proposed neural model achieves 91.7% accuracy on the Yelp datasets, outperforming traditional discrete models and neural baseline systems by a large margin.