Dual Memory Network Model for Biased Product Review Classification

Abstract

In sentiment analysis (SA) of product reviews, both user and product information are proven to be useful. Current tasks handle user pro- file and product information in a unified model which may not be able to learn salient fea- tures of users and products effectively. In this work, we propose a dual user and product memory network (DUPMN) model to learn user profiles and product reviews using separate memory networks. Then, the two repre- sentations are used jointly for sentiment pre- diction. The use of separate models aims to capture user profiles and product information more effectively. Compared to state-of-the-art unified prediction models, the evaluations on three benchmark datasets, IMDB, Yelp13, and Yelp14, show that our dual learning model gives performance gain of 0.6%, 1.2%, and 0.9%, respectively. The improvements are also deemed very significant measured by p-values.