Stance Classification in Texts from Blogs on the 2016 British Referendum

Research output: Chapter in Book/Report/Conference proceedingPaper in conference proceeding


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.


External organisations
  • Linnaeus University
Research areas and keywords

Subject classification (UKÄ) – MANDATORY

  • General Language Studies and Linguistics
Original languageEnglish
Title of host publicationSpeech and computer
Subtitle of host publication19th International Conference, SPECOM 2017, Hatfield, UK, September 12-16, 2017, Proceedings
ISBN (Electronic)978-3-319-66429-3
Publication statusPublished - 2017
Publication categoryResearch

Publication series

NameLectures in Artificial Intelligence
PublisherSpringer International Publishing

Related projects

Carita Paradis, Andreas Kerren, Magnus Sahlgren, Kostiantyn Kucher, Maria Skeppstedt & Vasiliki Simaki

Swedish Research Council


Project: Research

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