A Semi-Supervised Approach to Detecting Stance in Tweets
This work addresses stance detection in social media for tasks like SemEval-2016, but it is incremental as it builds on existing methods with a new dataset creation approach.
The authors tackled stance classification in tweets by creating a large, topic-specific training corpus using high-precision hashtags to avoid human labeling, achieving good performance with features based on opinion-target pairs and sentiment lexicons.
Stance classification aims to identify, for a particular issue under discussion, whether the speaker or author of a conversational turn has Pro (Favor) or Con (Against) stance on the issue. Detecting stance in tweets is a new task proposed for SemEval-2016 Task6, involving predicting stance for a dataset of tweets on the topics of abortion, atheism, climate change, feminism and Hillary Clinton. Given the small size of the dataset, our team created our own topic-specific training corpus by developing a set of high precision hashtags for each topic that were used to query the twitter API, with the aim of developing a large training corpus without additional human labeling of tweets for stance. The hashtags selected for each topic were predicted to be stance-bearing on their own. Experimental results demonstrate good performance for our features for opinion-target pairs based on generalizing dependency features using sentiment lexicons.