Dual memory network model for sentiment analysis of review text

Jia Xing SHEN, Mingyu Derek MA, Rong XIANG, Qin LU , Elvira Perez VALLEJOS, Ge XU , Chu Ren HUANG, Yunfei LONG*

*Corresponding author for this work

Research output: Journal PublicationsJournal Article (refereed)peer-review

14 Citations (Scopus)

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.

Original languageEnglish
Article number105004
Number of pages11
JournalKnowledge-Based Systems
Volume188
DOIs
Publication statusPublished - 5 Jan 2020
Externally publishedYes

Bibliographical note

The work is partially supported by the research grants from Hong Kong Polytechnic University (PolyU RTVU) and GRF, Hong Kong grant (CERG PolyU 15211/14E , PolyU 152006/16E ).

Yunfei Long and Elvira Perez Vallejos acknowledge the financial support of the NIHR Nottingham Biomedical Research Centre and NIHR MindTech Healthcare Technology Co-operative .

Keywords

  • Attention mechanism
  • Heterogeneous network
  • Network embedding
  • Text processing

Fingerprint

Dive into the research topics of 'Dual memory network model for sentiment analysis of review text'. Together they form a unique fingerprint.

Cite this