Stance detection in online discussions
This work addresses stance detection for online content analysis, but it is incremental as it adapts an existing method to a new language and domain.
The paper tackled stance detection in online discussions by adapting a maximum entropy classifier with various features from English tweets to Czech news commentaries, achieving unspecified performance.
This paper describes our system created to detect stance in online discussions. The goal is to identify whether the author of a comment is in favor of the given target or against. Our approach is based on a maximum entropy classifier, which uses surface-level, sentiment and domain-specific features. The system was originally developed to detect stance in English tweets. We adapted it to process Czech news commentaries.