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Hierarchical viewpoint discovery from tweets using Bayesian modelling

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Zhu, Lixing, He, Yulan and Zhou, Deyu (2019) Hierarchical viewpoint discovery from tweets using Bayesian modelling. Expert Systems with Applications, 116 . pp. 430-438. doi:10.1016/j.eswa.2018.09.028

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Official URL: http://dx.doi.org/10.1016/j.eswa.2018.09.028

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Abstract

When users express their stances towards a topic in social media, they might elaborate their viewpoints or reasoning. Oftentimes, viewpoints expressed by different users exhibit a hierarchical structure. Therefore, detecting this kind of hierarchical viewpoints offers a better insight to understand the public opinion. In this paper, we propose a novel Bayesian model for hierarchical viewpoint discovery from tweets. Driven by the motivation that a viewpoint expressed in a tweet can be regarded as a path from the root to a leaf of a hierarchical viewpoint tree, the assignment of the relevant viewpoint topics is assumed to follow two nested Chinese restaurant processes. Moreover, opinions in text are often expressed in un-semantically decomposable multi-terms or phrases, such as ‘economic recession’. Hence, a hierarchical Pitman–Yor process is employed as a prior for modelling the generation of phrases with arbitrary length. Experimental results on two Twitter corpora demonstrate the effectiveness of the proposed Bayesian model for hierarchical viewpoint discovery.

Item Type: Journal Article
Subjects: Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software
Divisions: Faculty of Science > Computer Science
Library of Congress Subject Headings (LCSH): Natural language processing (Computer science), Social media, Public opinion
Journal or Publication Title: Expert Systems with Applications
Publisher: Pergamon-Elsevier Science Ltd.
ISSN: 0957-4174
Official Date: February 2019
Dates:
DateEvent
February 2019Published
13 September 2018Available
12 September 2018Accepted
Volume: 116
Page Range: pp. 430-438
DOI: 10.1016/j.eswa.2018.09.028
Status: Peer Reviewed
Publication Status: Published
Access rights to Published version: Restricted or Subscription Access
RIOXX Funder/Project Grant:
Project/Grant IDRIOXX Funder NameFunder ID
61528302[NSFC] National Natural Science Foundation of Chinahttp://dx.doi.org/10.13039/501100001809
61772132[NSFC] National Natural Science Foundation of Chinahttp://dx.doi.org/10.13039/501100001809
BK20161430Natural Science Foundation of Jiangsu Provincehttp://dx.doi.org/10.13039/501100004608
103652Innovate UKUNSPECIFIED

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