SemEval-2016 Task 6: Detecting Stance in Tweets

• DOI: 10.18653/v1/s16-1003
• Publication Date: 2016-07-14T04:07:26Z
• Authors: Saif Mohammad; Svetlana Kiritchenko; Parinaz Sobhani; Xiaodan Zhu; Colin Cherry
• Keywords: SemEval

Here for the first time we present a shared task on detecting stance from tweets: given tweet and target entity (person, organization, etc.), automatic natural language systems must determine whether tweeter is in favor of target, against or neither inference likely.The interest may not be referred to tweet, it opinion.Two tasks are proposed.Task A traditional supervised classification where 70% annotated data used as training rest testing.For Task B, use test all instances new (not A) no provided.Our received submissions 19 teams 9 B. The highest F-score obtained was 67.82 56.28 However, found markedly more difficult infer towards tweets that express opinion another entity.