@InProceedings{john-vechtomova:2017:WASSA2017,
  author    = {John, Vineet  and  Vechtomova, Olga},
  title     = {UWat-Emote at EmoInt-2017: Emotion Intensity Detection using Affect Clues, Sentiment Polarity and Word Embeddings},
  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     = {249--254},
  abstract  = {This paper describes the UWaterloo affect prediction system developed for
	EmoInt-2017. We delve into our feature selection approach for affect intensity,
	affect presence, sentiment intensity and sentiment presence lexica alongside
	pre-trained word embeddings, which are utilized to extract emotion intensity
	signals from tweets in an ensemble learning approach. The system employs
	emotion specific model training, and utilizes distinct models for each of the
	emotion corpora in isolation. Our system utilizes gradient boosted regression
	as the primary learning technique to predict the final emotion intensities.},
  url       = {http://www.aclweb.org/anthology/W17-5235}
}