Detecting stance in tweets and analyzing its interaction with sentiment

DOIResolve DOI: http://doi.org/10.18653/v1/S16-2021
AuthorSearch for: ; Search for: ; Search for:
TypeArticle
Proceedings titleProceedings of the Fifth Joint Conference on Lexical and Computational Semantics
ConferenceFifth Joint Conference on Lexical and Computational Semantics, August 2016, Berlin, Germany
AbstractOne may express favor (or disfavor) towards a target by using positive or negative language. Here for the first time we present a dataset of tweets annotated for whether the tweeter is in favor of or against pre-chosen targets, as well as for sentiment. These targets may or may not be referred to in the tweets, and they may or may not be the target of opinion in the tweets. We develop a simple stance detection system that outper-forms all 19 teams that participated in a recent shared task competition on the same dataset (SemEval-2016 Task #6). Additionally , access to both stance and sentiment annotations allows us to conduct several experiments to tease out their interactions. We show that while sentiment features are useful for stance classification, they alone are not sufficient. We also show the impacts of various features on detecting stance and sentiment, respectively.
Publication date
PublisherAssociation for Computational Linguistics
LanguageEnglish
AffiliationInformation and Communication Technologies; National Research Council Canada
Peer reviewedYes
NPARC number23001909
Export citationExport as RIS
Report a correctionReport a correction
Record identifier84476721-b81e-4280-91e7-8ed2f5de4712
Record created2017-05-24
Record modified2017-05-24
Bookmark and share
  • Share this page with Facebook (Opens in a new window)
  • Share this page with Twitter (Opens in a new window)
  • Share this page with Google+ (Opens in a new window)
  • Share this page with Delicious (Opens in a new window)
Date modified: