@InProceedings{santos-vieira:2017:WASSA2017,
  author    = {Santos, Henrique  and  Vieira, Renata},
  title     = {PLN-PUCRS at EmoInt-2017: Psycholinguistic features for emotion intensity prediction in tweets},
  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     = {189--192},
  abstract  = {Linguistic Inquiry and Word Count (LIWC) is a rich dictionary that map words
	into several psychological categories such as Affective, Social, Cognitive,
	Perceptual and Biological processes. In this work, we have used LIWC
	psycholinguistic categories to train regression models and predict emotion
	intensity in tweets for the EmoInt-2017 task. Results show that LIWC features
	may boost emotion intensity prediction on the basis of a low dimension set.},
  url       = {http://www.aclweb.org/anthology/W17-5225}
}