@InProceedings{mohammad-bravomarquez:2017:WASSA2017,
  author    = {Mohammad, Saif  and  Bravo-Marquez, Felipe},
  title     = {WASSA-2017 Shared Task on Emotion Intensity},
  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     = {34--49},
  abstract  = {We present the first shared task on detecting the intensity of emotion felt by
	the speaker of a tweet. We create the first datasets of tweets annotated for
	anger, fear, joy, and sadness intensities using a technique called best--worst
	scaling (BWS). We show that the annotations lead to reliable fine-grained
	intensity scores (rankings of tweets by intensity). The data was partitioned
	into training, development, and test sets for the competition. Twenty-two
	teams participated in the shared task, with the best system obtaining a Pearson
	correlation of 0.747 with the gold intensity scores. We summarize the machine
	learning setups, resources, and tools used by the participating teams, with a
	focus on the techniques and resources that are particularly useful for the
	task. The emotion intensity dataset and the shared task are helping improve our
	understanding of how we convey more or less intense emotions through language.},
  url       = {http://www.aclweb.org/anthology/W17-5205}
}