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dc.contributor.author Shenoy, Gourav Ganesh
dc.contributor.author Dsouza, Erika Helda
dc.contributor.author Kübler, Sandra
dc.date.accessioned 2017-03-06T19:23:04Z
dc.date.available 2017-03-06T19:23:04Z
dc.date.issued 2016
dc.identifier.uri http://hdl.handle.net/2022/21252
dc.description.abstract 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. en
dc.language.iso en_US en
dc.subject Computation and Language en
dc.subject Natural Language Processing en
dc.subject Stance Detection en
dc.title Performing Stance Detection on Twitter Data using Computational Linguistics Techniques en
dc.type Preprint en
dc.type Technical Report en
dc.type Working Paper en


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