@InProceedings{schuff-EtAl:2017:WASSA2017,
  author    = {Schuff, Hendrik  and  Barnes, Jeremy  and  Mohme, Julian  and  Pad\'{o}, Sebastian  and  Klinger, Roman},
  title     = {Annotation, Modelling and Analysis of Fine-Grained Emotions on a Stance and Sentiment Detection Corpus},
  booktitle = {Proceedings of the 8th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis},
  month     = {September},
  year      = {2017},
  address   = {Copenhagen, Denmark},
  publisher = {Association for Computational Linguistics},
  pages     = {13--23},
  abstract  = {There is a rich variety of data sets for sentiment analysis
	  (viz.,~polarity and subjectivity classification). For the more
	  challenging task of detecting discrete emotions following the
	  definitions of Ekman and Plutchik, however, there are much fewer
	  data sets, and notably no resources for the social media
	  domain. This paper contributes to closing this gap by extending the
	  \textit{SemEval 2016 stance and sentiment dataset} with emotion
	  annotation. We (a) analyse annotation reliability and annotation
	  merging; (b) investigate the relation between emotion annotation and
	  the other annotation layers (stance, sentiment); (c) report
	  modelling results as a baseline for future work.},
  url       = {http://www.aclweb.org/anthology/W17-5203}
}