@InProceedings{mohammad-bravomarquez:2017:starSEM,
  author    = {Mohammad, Saif  and  Bravo-Marquez, Felipe},
  title     = {Emotion Intensities in Tweets},
  booktitle = {Proceedings of the 6th Joint Conference on Lexical and Computational Semantics (*SEM 2017)},
  month     = {August},
  year      = {2017},
  address   = {Vancouver, Canada},
  publisher = {Association for Computational Linguistics},
  pages     = {65--77},
  abstract  = {This paper examines the task of detecting intensity of emotion from text. We
	create the first datasets of tweets annotated for anger, fear, joy, and sadness
	intensities. We use a technique called best--worst scaling (BWS) that improves
	annotation consistency and obtains reliable fine-grained scores. We show that
	emotion-word hashtags often impact emotion intensity, usually conveying a more
	intense emotion. Finally, we create a benchmark regression system and conduct
	experiments to determine: which features are useful for detecting emotion
	intensity; and, the extent to which two emotions are similar in terms of how
	they manifest in language.},
  url       = {http://www.aclweb.org/anthology/S17-1007}
}