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BibTeX

@InProceedings{schuff17:_annot_model_and_analy_of,
  author = {Hendrik Schuff and Jeremy Barnes and Julian Mohme and Sebastian Padó and Roman Klinger},
  title = 	 {Annotation, Modelling and Analysis of Fine-Grained
          Emotions on a Stance and Sentiment Detection Corpus},
  booktitle = {Proceedings of the EMNLP WASSA workshop},
  keywords =  {workshop myown},
  year = 	 2017,
  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 = {https://www.aclweb.org/anthology/W17-5203.pdf},
  address = 	 {Copenhagen, Denmark}}