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Evaluating stance-annotated sentences from the Brexit Blog Corpus: A quantitative linguistic analysis
Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM), 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 and media technology (CM), Department of Computer Science. (ISOVIS)ORCID iD: 0000-0002-0519-2537
2018 (English)In: ICAME Journal/International Computer Archive of Modern English, ISSN 0801-5775, E-ISSN 1502-5462, Vol. 42, no 1, p. 133-166Article in journal (Refereed) Published
Abstract [en]

This paper offers a formally driven quantitative analysis of stance-annotated sentences in the Brexit Blog Corpus (BBC). Our goal is to highlight linguistic features that determine the formal profiles of six stance categories (contrariety, hypotheticality, necessity, prediction, source of knowledge and uncertainty) in a subset of the BBC. The study has two parts: firstly, it examines a large number of formal linguistic features that occur in the sentences in order to describe the specific characteristics of each category, and secondly, it compares characteristics in the entire data set in order to determine linguistic similarities throughout the data set. We show that among the six stance categories in the corpus, contrariety and necessity are the most discriminative ones, with the former using longer sentences, more conjunctions, more repetitions and shorter forms than the sentences expressing other stances. The latter has longer lexical forms but shorter sentences, which are syntactically more complex. We show that stance in our data set is expressed in sentences with around 21 words per sentence. The sentences consist mainly of alphabetical characters forming a varied vocabulary without special forms, such as digits or special characters.

Place, publisher, year, edition, pages
De Gruyter Open, 2018. Vol. 42, no 1, p. 133-166
Keywords [en]
stance-taking, corpus annotation, political blog text, statistical analysis, formal features
National Category
Language Technology (Computational Linguistics) Specific Languages
Research subject
Computer Science, Information and software visualization
Identifiers
URN: urn:nbn:se:lnu:diva-70768DOI: 10.1515/icame-2018-0007OAI: oai:DiVA.org:lnu-70768DiVA, id: diva2:1182076
Projects
StaViCTA
Funder
Swedish Research Council, 2012-5659Available from: 2018-02-12 Created: 2018-02-12 Last updated: 2018-04-19

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Simaki, VasilikiParadis, CaritaKerren, Andreas

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CiteExportLink to record
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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
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