Debate Stance Classification Using Word Embeddings

Konjengbam, Anand and Ghosh, Subrata and Kumar, Nagendra and Singh, Manish (2018) Debate Stance Classification Using Word Embeddings. In: Big Data Analytics and Knowledge Discovery. Lecture Notes in Computer Science, 11031 (11031). Springer, pp. 382-395. ISBN 9783319985398

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Abstract

Online debate sites act as a popular platform for users to express and form opinions. In this paper, we propose a novel unsupervised approach to perform stance classification of two-sided online debate posts. We propose the use of word embeddings to address the problem of identifying the preferred target of each aspect. We also use word embeddings to train a supervised classifier for selecting only target related aspects. The aspect-target preference information is used to model the stance classification task as an integer linear programming problem. The classifier gives an average aspect classification accuracy of 84% on multiple datasets. Our word embedding based stance classification approach gives 19.80% higher user stance classification accuracy (F1-score) compared to the existing methods. Our results suggest that the use of word embeddings improves accuracy and enables us to perform stance classification without the need for external domain-specific information.

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