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Adversarial learning of poisson factorisation model for gauging brand sentiment in user reviews
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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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Official URL: https://aclanthology.org/2021.eacl-main.199/
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) | ||||||||||||
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Subjects: | P Language and Literature > P Philology. Linguistics Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software |
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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: |
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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 | ||||||||||||
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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 | ||||||||||||
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