IDI@NTNU at SemEval-2016 task 6: Detecting stance in tweets using shallow features and GloVe vectors for word representation

Abstract

This paper describes an approach to automatically detect stance in tweets by building a supervised system combining shallow features and pre-trained word vectors as word representation. The word vectors were obtained from several collections of large corpora using GloVe, an unsupervised learning algorithm. We created feature vectors by selecting the word vectors relevant to the data and summing them for each unique word. Combining multiple classifiers into a voting classifier, representing the best of both approaches, shows a significant improvement over the baseline system.