Stance Classification in Texts from Blogs on the 2016 British Referendum

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

Standard

Stance Classification in Texts from Blogs on the 2016 British Referendum. / Simaki, Vasiliki; Paradis, Carita; Kerren, Andreas.

Speech and computer: 19th International Conference, SPECOM 2017, Hatfield, UK, September 12-16, 2017, Proceedings. 2017. p. 700-709 (Lectures in Artificial Intelligence; Vol. 10458).

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

Harvard

Simaki, V, Paradis, C & Kerren, A 2017, Stance Classification in Texts from Blogs on the 2016 British Referendum. in Speech and computer: 19th International Conference, SPECOM 2017, Hatfield, UK, September 12-16, 2017, Proceedings. Lectures in Artificial Intelligence, vol. 10458, pp. 700-709. https://doi.org/10.1007/978-3-319-66429-3_70

APA

Simaki, V., Paradis, C., & Kerren, A. (2017). Stance Classification in Texts from Blogs on the 2016 British Referendum. In Speech and computer: 19th International Conference, SPECOM 2017, Hatfield, UK, September 12-16, 2017, Proceedings (pp. 700-709). (Lectures in Artificial Intelligence; Vol. 10458). https://doi.org/10.1007/978-3-319-66429-3_70

CBE

Simaki V, Paradis C, Kerren A. 2017. Stance Classification in Texts from Blogs on the 2016 British Referendum. In Speech and computer: 19th International Conference, SPECOM 2017, Hatfield, UK, September 12-16, 2017, Proceedings. pp. 700-709. (Lectures in Artificial Intelligence). https://doi.org/10.1007/978-3-319-66429-3_70

MLA

Simaki, Vasiliki, Carita Paradis and Andreas Kerren "Stance Classification in Texts from Blogs on the 2016 British Referendum". Speech and computer: 19th International Conference, SPECOM 2017, Hatfield, UK, September 12-16, 2017, Proceedings. Lectures in Artificial Intelligence. 2017, 700-709. https://doi.org/10.1007/978-3-319-66429-3_70

Vancouver

Simaki V, Paradis C, Kerren A. Stance Classification in Texts from Blogs on the 2016 British Referendum. In Speech and computer: 19th International Conference, SPECOM 2017, Hatfield, UK, September 12-16, 2017, Proceedings. 2017. p. 700-709. (Lectures in Artificial Intelligence). https://doi.org/10.1007/978-3-319-66429-3_70

Author

Simaki, Vasiliki ; Paradis, Carita ; Kerren, Andreas. / Stance Classification in Texts from Blogs on the 2016 British Referendum. Speech and computer: 19th International Conference, SPECOM 2017, Hatfield, UK, September 12-16, 2017, Proceedings. 2017. pp. 700-709 (Lectures in Artificial Intelligence).

RIS

TY - GEN

T1 - Stance Classification in Texts from Blogs on the 2016 British Referendum

AU - Simaki, Vasiliki

AU - Paradis, Carita

AU - Kerren, Andreas

PY - 2017

Y1 - 2017

N2 - 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.

AB - 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.

U2 - 10.1007/978-3-319-66429-3_70

DO - 10.1007/978-3-319-66429-3_70

M3 - Paper in conference proceeding

SN - 978-3-319-66428-6

T3 - Lectures in Artificial Intelligence

SP - 700

EP - 709

BT - Speech and computer

ER -