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.
CITATION STYLE
B, A. J. N., Tak, R., & Patil, H. A. (2017). Novel Phase Encoded Mel Cepstral Features, 1(Mci), 572–581.
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