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.
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Acknowledgments
This research is part of the StaViCTA project [22], supported by the Swedish Research Council (framework grant “the Digitized Society – Past, Present, and Future”, No. 2012-5659). We thank Maria Skeppstedt and Tom Sköld for the data annotation, and Kostiantyn Kucher for the implementation of the annotation tool.
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Simaki, V., Paradis, C., Kerren, A. (2017). Stance Classification in Texts from Blogs on the 2016 British Referendum. In: Karpov, A., Potapova, R., Mporas, I. (eds) Speech and Computer. SPECOM 2017. Lecture Notes in Computer Science(), vol 10458. Springer, Cham. https://doi.org/10.1007/978-3-319-66429-3_70
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DOI: https://doi.org/10.1007/978-3-319-66429-3_70
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