Extracting Contextonyms from Twitter for Stance Detection.

Opinion mining on tweets is a challenge: short texts, implicit topics, inventive spellings and new vocabulary are the rule. We aim at efficiently determining the stance of tweets towards a given target. We propose a method using the concept of contextonyms and contextosets in order to disambiguate implicit content and improve a given stance classifier. Contextonymy is extracted from a word co-occurrence graph, and allows to grasp the sense of a word according to its surrounding words. We evaluate our method on a freely available annotated tweet corpus, used to benchmark stance detection on tweets during SemEval2016.