Zhao, Runcong, Gui, Lin, Pergola, Gabriele and He, Yulan (2021) Adversarial learning of poisson factorisation model for gauging brand sentiment in user reviews. In: EACL 2021 : The 16th Conference of the European Chapter of the Association for Computational Linguistics, Virtual conference, 19-23 Apr 2021. Published in: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics : Main Volume pp. 2341-2351.

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Abstract

In this paper, we propose the Brand-Topic Model (BTM) which aims to detect brand-associated polarity-bearing topics from product reviews. Different from existing models for sentiment-topic extraction which assume topics are grouped under discrete sentiment categories such as `positive', `negative' and `neural', BTM is able to automatically infer real-valued brand-associated sentiment scores and generate fine-grained sentiment-topics in which we can observe continuous changes of words under a certain topic (e.g., `shaver' or `cream') while its associated sentiment gradually varies from negative to positive. BTM is built on the Poisson factorisation model with the incorporation of adversarial learning. It has been evaluated on a dataset constructed from Amazon reviews. Experimental results show that BTM outperforms a number of competitive baselines in brand ranking, achieving a better balance of topic coherence and uniqueness, and extracting better-separated polarity-bearing topics.

Item Type:	Conference Item (Paper)
Subjects:	P Language and Literature > P Philology. Linguistics Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software
Divisions:	Faculty of Science, Engineering and Medicine > Science > Computer Science
Library of Congress Subject Headings (LCSH):	Computational Linguistics, Data mining, Sentiment analysis , Natural language processing (Computer science), Text processing (Computer science) , Keyword searching -- Technological innovations
Journal or Publication Title:	Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics : Main Volume
Publisher:	Association for Computational Linguistics
Official Date:	August 2021
Dates:	Date Event August 2021 Published 11 January 2021 Accepted
Page Range:	pp. 2341-2351
Status:	Peer Reviewed
Publication Status:	Published
Access rights to Published version:	Open Access (Creative Commons)
Copyright Holders:	©2021 Association for Computational Linguistics
Date of first compliant deposit:	5 February 2021
Date of first compliant Open Access:	2 September 2021
Funder:
RIOXX Funder/Project Grant:	Project/Grant ID RIOXX Funder Name Funder ID EP/T017112/1 [EPSRC] Engineering and Physical Sciences Research Council http://dx.doi.org/10.13039/501100000266 EP/V048597/1 [EPSRC] Engineering and Physical Sciences Research Council http://dx.doi.org/10.13039/501100000266 EP/V020579/1 UK Research and Innovation http://dx.doi.org/10.13039/100014013
Conference Paper Type:	Paper
Title of Event:	EACL 2021 : The 16th Conference of the European Chapter of the Association for Computational Linguistics
Type of Event:	Conference
Location of Event:	Virtual conference
Date(s) of Event:	19-23 Apr 2021
Related URLs:	Organisation Publisher
Open Access Version:	ArXiv

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