Performing Stance Detection on Twitter Data using Computational Linguistics Techniques

As humans, we can often detect from a person’s utterances if he/she is in favor of or against a given target entity (topic, product, another person, etc). But from the perspective of a computer, we need means to automatically deduce the stance of the tweeter, given just the tweet text. In this paper, we present our results of performing stance detection on twitter data using a supervised approach. We begin by extracting bag-of-words to perform classification using TIMBL, then try and optimize the features to improve stance detection accuracy, followed by extending the dataset with two sets of lexicons arguing, and MPQA subjectivity; next we explore the MALT parser and construct features using its dependency triples, finally we perform analysis using Scikit-learn Random Forest implementation.