Final published version
Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
}
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: contrariety, hypotheticality, necessity, 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 contrariety and necessity 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: contrariety, hypotheticality, necessity, 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 contrariety and necessity binary classification achieved the best results with up to 71% accuracy.
KW - Stance-taking
KW - Text classification
KW - Political blogs
KW - BREXIT
U2 - 10.1007/978-3-319-66429-3_70
DO - 10.1007/978-3-319-66429-3_70
M3 - Conference contribution/Paper
SN - 9783319664286
T3 - Lecture Notes in Computer Science
SP - 700
EP - 709
BT - Proceedings of the 19th International Conference on Speech and Computer – SPECOM 2017
A2 - Karpov, Alexey
A2 - Potapova, Rodmonga
A2 - Mporas, Iosif
PB - Springer
CY - Cham
ER -