SGMR: Sentiment-aligned generative model for reviews

Abstract

Customers rate their purchases and leave comments when buying products from e-commerce web sites. However, the commentary information did not draw enough attention until recently. Users’ reviews contain much more information than other behaviors and the review text shows the characteristics of both products and users. Users’ comments are more likely to express their attitudes towards the purchasing. These sentiment tendencies will affect users when buying new products or rating products. In this paper, we propose Sentiment-aligned Generative Model for Reviews (SGMR) to combine rating dimensions with sentiment dimensions in users’ reviews. We extract sentiment topics using opinion mining methods. A generative feature reviews model based on sentiment is subsequently constructed. Finally the rating dimensions and sentiment dimensions align with each other with Factorization Machines (FM) model. Our model generates interpretable sentiment topics for latent sentiment dimensions. Experiments on real world datasets show that our proposed model leads to significant improvements compared with other baselines. Furthermore, our opinions have been confirmed that comments will affect other users’ purchasing and rating.