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Stance Classification in Texts from Blogs on the 2016 British Referendum

Simaki, Vasiliki LU ; Paradis, Carita LU and Kerren, Andreas (2017) In Speech and computer 10458. p.700-709
Abstract
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... (More)
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. (Less)
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author
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
in
Speech and computer
volume
10458
pages
700 - 709
ISBN
978-3-319-66428-6
language
English
LU publication?
yes
id
ebcf51e3-442a-4eca-a526-f5acb94651ff
date added to LUP
2017-06-02 19:46:08
date last changed
2017-08-30 16:07:51
@inbook{ebcf51e3-442a-4eca-a526-f5acb94651ff,
  abstract     = {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. },
  author       = {Simaki, Vasiliki and Paradis, Carita and Kerren, Andreas},
  isbn         = {978-3-319-66428-6},
  language     = {eng},
  pages        = {700--709},
  series       = {Speech and computer},
  title        = {Stance Classification in Texts from Blogs on the 2016 British Referendum},
  volume       = {10458},
  year         = {2017},
}