Learning representations from heterogeneous network for sentiment classification of product reviews


There have been increasing interests in natural language processing to explore effective methods in learning better representations of text for sentiment classification in product reviews. However, most existing methods do not consider subtle interplays among words appeared in review text, authors of reviews and products the reviews are associated with. In this paper, we make use of a heterogeneous network to model the shared polarity in product reviews and learn representations of users, products they commented on and words they used simultaneously. The basic idea is to first construct a heterogeneous network which links users, products, words appeared in product reviews, as well as the polarities of the words. Based on the constructed network, representations of nodes are learned using a network embedding method, which are subsequently incorporated into a convolutional neural network for sentiment analysis. Evaluations on the product reviews, including IMDB, Yelp 2013 and Yelp 2014 datasets, show that the proposed approach achieves the state-of-the-art performance.

Publication DOI: https://doi.org/10.1016/j.knosys.2017.02.030
Divisions: College of Engineering & Physical Sciences > Systems analytics research institute (SARI)
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Additional Information: © 2017, Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0/
Uncontrolled Keywords: sentiment classification,representation learning,product reviews,network embedding,Management Information Systems,Software,Information Systems and Management,Artificial Intelligence
Publication ISSN: 1872-7409
Last Modified: 08 Jul 2024 07:29
Date Deposited: 13 Mar 2017 15:45
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Related URLs: http://www.scop ... tnerID=8YFLogxK (Scopus URL)
https://www.sci ... 1144?via%3Dihub (Publisher URL)
PURE Output Type: Article
Published Date: 2017-05-15
Published Online Date: 2017-02-28
Accepted Date: 2017-02-27
Submitted Date: 2016-10-28
Authors: Gui, Lin
Zhou, Yu
Xu, Ruifeng
He, Yulan (ORCID Profile 0000-0003-3948-5845)
Lu, Qin

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