Stance Classification in Texts from Blogs on the 2016 British Referendum

Simaki, Vasiliki and Paradis, Carita and Kerren, Andreas (2017) Stance Classification in Texts from Blogs on the 2016 British Referendum. In: Proceedings of the 19th International Conference on Speech and Computer – SPECOM 2017 :. Lecture Notes in Computer Science . Springer, Cham, pp. 700-709. ISBN 9783319664286

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: 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.