Dual Memory Network Model for Biased Product Review Classification
This work addresses sentiment analysis for product reviews by improving classification accuracy, though it is incremental as it builds on existing memory network approaches.
The paper tackled biased product review classification by proposing a dual memory network model to separately learn user and product features, achieving performance gains of 0.6%, 1.2%, and 0.9% on three benchmark datasets compared to state-of-the-art unified models.
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.