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Stance Classification in Texts from Blogs on the 2016 British Referendum
Linnaeus University, Faculty of Technology, Department of Computer Science. Lund University. (ISOVIS)ORCID iD: 0000-0002-8998-3618
Lund University.ORCID iD: 0000-0002-7240-9003
Linnaeus University, Faculty of Technology, Department of Computer Science. (ISOVIS)ORCID iD: 0000-0002-0519-2537
2017 (English)In: Speech and Computer: 19th International Conference, SPECOM 2017, Hatfield, UK, September 12-16, 2017, Proceedings / [ed] Alexey Karpov, Rodmonga Potapova, and Iosif Mporas, Springer International Publishing , 2017, 700-709 p.Conference paper, Published paper (Refereed)
Abstract [en]

The problem of identifying and correctly attributing speaker stance in human communication is addressed in this paper. The data set consists of political blogs dealing with the 2016 British referendum. A cognitive-functional framework is adopted with data annotated for six notional stance categories: concession/contrariness, hypotheticality, need/ requirement, prediction, source of knowledge, and uncertainty. We show that these categories can be implemented in a text classification task and automatically detected. To this end, we propose a large set of lexical and syntactic linguistic features. These features were tested and classification experiments were implemented using different algorithms. We achieved accuracy of up to 30% for the six-class experiments, which is not fully satisfactory. As a second step, we calculated the pair-wise combinations of the stance categories. The concession/contrariness and need/requirement binary classification achieved the best results with up to 71% accuracy. 

Place, publisher, year, edition, pages
Springer International Publishing , 2017. 700-709 p.
Series
Lecture Notes in Artificial Intelligence, ISSN 0302-9743 ; 10458
Keyword [en]
stance-taking, text classification, political blogs, BREXIT
National Category
Language Technology (Computational Linguistics) Specific Languages
Research subject
Computer and Information Sciences Computer Science, Computer Science; Humanities, Linguistics
Identifiers
URN: urn:nbn:se:lnu:diva-64580DOI: 10.1007/978-3-319-66429-3_70ISBN: 978-3-319-66428-6 (print)ISBN: 978-3-319-66429-3 (electronic)OAI: oai:DiVA.org:lnu-64580DiVA: diva2:1104280
Conference
19th International Conference on Speech and Computer (SPECOM '17), 12-16 September 2017, Hatfield, Hertfordshire, UK
Projects
StaViCTA
Funder
Swedish Research Council, 2012-5659
Available from: 2017-05-31 Created: 2017-05-31 Last updated: 2017-10-19Bibliographically approved

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Simaki, VasilikiParadis, CaritaKerren, Andreas
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CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf