Dual Memory Network Model for Sentiment Analysis of Review Text


In sentiment analysis of product reviews, both user and product information are proven to be useful. Current works handle user profile and product information in a unified model which may not be able to learn salient features 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 information for reviews classification using separate memory networks. Then, the two representations are used jointly for sentiment analysis. The use of separate models aims to capture user profiles and product information more effectively. Comparing with state-of-the-art unified prediction models, 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.

Knowledge-Based Systems
Jiaxing Shen
Research Assistant Professor
Hong Kong PolyU
Mingyu Derek Ma
Mingyu Derek Ma
PhD Student
UCLA Computer Science
Rong Xiang
PhD Candidate
Hong Kong PolyU
Qin Lu
Hong Kong PolyU
Elvira Perez Vallejos
Associate Professor
University of Nottingham
Chu-Ren Huang
Chair Professor
Hong Kong PolyU Linguistics
Yunfei Long
University of Essex