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Dual memory network model for sentiment analysis of review text

Shen, JiaXing; Ma, Mingyu Derek; Xiang, Rong; Lu, Qin; Vallejos, Elvira Perez; Xu, Ge; Long, Yunfei; Huang, Chu-Ren

Dual memory network model for sentiment analysis of review text Thumbnail


Authors

JiaXing Shen

Mingyu Derek Ma

Rong Xiang

Qin Lu

Ge Xu

Yunfei Long

Chu-Ren Huang



Abstract

In sentiment analysis of product reviews, both user and product information are proven to be useful. Current works 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 information for reviews classification using separate memory networks. Then, the two representations are used jointly for sentiment analysis. The use of separate models aims to capture user profiles and product information more effectively. Comparing with state-of-the-art unified prediction models, 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.

Citation

Shen, J., Ma, M. D., Xiang, R., Lu, Q., Vallejos, E. P., Xu, G., …Huang, C. (2019). Dual memory network model for sentiment analysis of review text. Knowledge-Based Systems, 188, Article 105004. https://doi.org/10.1016/j.knosys.2019.105004

Journal Article Type Article
Acceptance Date Aug 27, 2019
Online Publication Date Sep 6, 2019
Publication Date Sep 6, 2019
Deposit Date Oct 1, 2019
Publicly Available Date Mar 29, 2024
Journal Knowledge-Based Systems
Print ISSN 0950-7051
Electronic ISSN 1872-7409
Publisher Elsevier
Peer Reviewed Peer Reviewed
Volume 188
Article Number 105004
DOI https://doi.org/10.1016/j.knosys.2019.105004
Keywords Software; Information Systems and Management; Management Information Systems; Artificial Intelligence
Public URL https://nottingham-repository.worktribe.com/output/2647374
Publisher URL https://www.sciencedirect.com/science/article/pii/S0950705119304198

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