Considering user profile and product information in biased product review classification: A Dual Memory Network Model

Outstanding Project Award (only 1)
in Best Capstone Project Award Competition of Department of Computing

Related paper
in Knowledge-based Systems and EMNLP 2018 9th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis

Outstanding Work by Students
in PolyU Institutional Research Archive


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.

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