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

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

Capstone Project (Bachelor Thesis) of Mingyu Derek MA

Supervised by Prof. Qin LU

Outstanding Project Award
in Best Capstone Project Award Competition of Department of Computing
Related paper
to appear in EMNLP 2018 9th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis
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in PolyU Institutional Research Archive

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.

Yunfei Long*, Mingyu Ma*, Qin Lu, Rong Xiang, Chu-Ren Huang (*: Equal contribution) (2018). Dual Memory Network Model for Biased Product Review Classification. To appear in EMNLP Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis.

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Mingyu Derek MA
Research Intern at UC Santa Cruz, UG student at Hong Kong PolyU working on Language Technologies

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