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 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.

arXiv commentTo appear in 2018 EMNLP 9th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis

References in corpus (5)

• Convolutional Neural Networks for Sentence Classification
• Linguistically Regularized LSTMs for Sentiment Classification
• Aspect Level Sentiment Classification with Deep Memory Network
• Improving Review Representations with User Attention and Product Attention for Sentiment Classification
• Cached Long Short-Term Memory Neural Networks for Document-Level Sentiment Classification