Learning Document Representation for Deceptive Opinion Spam Detection

Abstract

Deceptive opinion spam in reviews of products or service is very harmful for customers in decision making. Existing approaches to detect deceptive spam are concern on feature designing. Hand-crafted features can show some linguistic phenomenon, but is time-consuming and can not reveal the connotative semantic meaning of the review. We present a neural network to learn document-level representation. In our model, we not only learn to represent each sentence but also represent the whole document of the review. We apply traditional convolutional neural network to represent the semantic meaning of sentences. We present two variant convolutional neural-network models to learn the document representation. The model taking sentence importance into consideration shows the better performance in deceptive spam detection which enhances the value of F1 by 5 %.