Stance Detection in Turkish Tweets

Stance detection is a classi€cation problem in natural language processing where for a text and target pair, a class result from the set {Favor, Against, Neither} is expected. It is similar to the sentiment analysis problem but instead of the sentiment of the text author, the stance expressed for a particular target is investigated in stance detection. In this paper, we present a stance detection tweet data set for Turkish comprising stance annotations of these tweets for two popular sports clubs as targets. Additionally, we provide the evaluation results of SVM classi€ers for each target on this data set, where the classi€ers use unigram, bigram, and hashtag features. Œis study is signi€cant as it presents one of the initial stance detection data sets proposed so far and the €rst one for Turkish language, to the best of our knowledge. Œe data set and the evaluation results of the corresponding SVM-based approaches will form plausible baselines for the comparison of future studies on stance detection.