Determining the stance expressed by an author from a post written for a two-sided debate in an online debate forum is a relatively new problem in opinion mining. We extend a state-of-the-art learningbased approach to debate stance classification by (1) inducing lexico-syntactic patterns based on syntactic dependencies and semantic frames that aim to capture the meaning of a sentence and provide a generalized representation of it; and (2) improving the classification of a test post via a novel way of exploiting the information in other test posts with the same stance. Empirical results on four datasets demonstrate the effectiveness of our extensions.
['Kazi Saidul Hasan', 'Vincent Ng']
Frame Semantics for Stance Classification