  As humans, we can often detect from a persons utterances if he or 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.
