Stance Classification in Texts from Blogs on the 2016 British Referendum
Simaki, Vasiliki LU ; Paradis, Carita LU
and Kerren, Andreas
(2017)
In Lectures in Artificial Intelligence
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)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/ebcf51e3-442a-4eca-a526-f5acb94651ff
- author
- Simaki, Vasiliki
LU
; Paradis, Carita
LU
and Kerren, Andreas
- organization
- publishing date
- 2017
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- host publication
- Speech and computer : 19th International Conference, SPECOM 2017, Hatfield, UK, September 12-16, 2017, Proceedings - 19th International Conference, SPECOM 2017, Hatfield, UK, September 12-16, 2017, Proceedings
- series title
- Lectures in Artificial Intelligence
- volume
- 10458
- pages
- 700 - 709
- external identifiers
-
- scopus:85029468464
- ISBN
- 978-3-319-66428-6
- 978-3-319-66429-3
- DOI
- 10.1007/978-3-319-66429-3_70
- project
- StaViCTA - Advances in the description and explanation of stance in discourse using visual and computational text analytics
- language
- English
- LU publication?
- yes
- id
- ebcf51e3-442a-4eca-a526-f5acb94651ff
- date added to LUP
- 2017-06-02 19:46:08
- date last changed
- 2024-06-09 17:48:15
@inproceedings{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}},
booktitle = {{Speech and computer : 19th International Conference, SPECOM 2017, Hatfield, UK, September 12-16, 2017, Proceedings}},
isbn = {{978-3-319-66428-6}},
language = {{eng}},
pages = {{700--709}},
series = {{Lectures in Artificial Intelligence}},
title = {{Stance Classification in Texts from Blogs on the 2016 British Referendum}},
url = {{http://dx.doi.org/10.1007/978-3-319-66429-3_70}},
doi = {{10.1007/978-3-319-66429-3_70}},
volume = {{10458}},
year = {{2017}},
}