Stance Detection in Tweets Using a Majority Vote Classifier

The task of stance detection is to determine whether someone is in favor or against a certain topic. A person may express the same stance towards a topic using positive or negative words. In this paper, several features and classifiers are explored to find out the combination that yields the best performance for stance detection. Due to the large number of features, ReliefF feature selection method was used to reduce the large dimensional feature space and improve the generalization capabilities. Experimental analyses were performed on five datasets, and the obtained results revealed that a majority vote classifier of the three classifiers: Random Forest, linear SVM and Gaussian Naive Bayes classifiers can be adopted for stance detection task.