Determining political interests of issue-motivated groups on social media: joint topic models for issues, sentiment and stance

Sandeepa Kannangara () and Wayne Wobcke ()
Additional contact information
Sandeepa Kannangara: University of New South Wales
Wayne Wobcke: University of New South Wales

Journal of Computational Social Science, 2022, vol. 5, issue 1, No 34, 840 pages

Abstract: Abstract Stance detection is an emerging research problem in opinion mining where the aim is to automatically determine from the text whether the author is for, against or neutral towards a proposition or target. In this paper, we propose a novel weakly supervised probabilistic topic model, Joint Issue-Sentiment-Stance Topic (JISST) model, for stance detection from political opinion in social media. The model automatically identifies the target issue and stance toward the target issue simultaneously from the text. Unlike other machine learning approaches to stance classification which require labelled data for training classifiers, JISST requires only a small number of seed words for each issue and stance and a sentiment lexicon. The model is evaluated on two datasets in the political domain: a Facebook dataset which contains posts of politically motivated Facebook groups in Australia and a Twitter dataset which was published for the SemEval 2016 competition. Experimental results demonstrate that JISST outperforms both weakly supervised and supervised baselines for stance and issue classification.

Keywords: Stance detection; Opinion mining; Social media analysis; Topic model (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://link.springer.com/10.1007/s42001-021-00146-4 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:jcsosc:v:5:y:2022:i:1:d:10.1007_s42001-021-00146-4

Ordering information: This journal article can be ordered from
http://www.springer. ... iences/journal/42001

DOI: 10.1007/s42001-021-00146-4

Access Statistics for this article

Journal of Computational Social Science is currently edited by Takashi Kamihigashi

More articles in Journal of Computational Social Science from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().