Debate Stance Classification Using Word Embeddings

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.