  In sentiment analysis (SA) of product reviews, both user and product
information are proven to be useful. Current tasks 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 reviews using separate memory networks. Then, the two representations
are used jointly for sentiment prediction. 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.
