@InProceedings{jain-EtAl:2017:WASSA2017,
  author    = {Goel, Pranav  and  Kulshreshtha, Devang  and  Jain, Prayas  and  Shukla, Kaushal Kumar},
  title     = {Prayas at EmoInt 2017: An Ensemble of Deep Neural Architectures 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     = {58--65},
  abstract  = {The paper describes the best performing system for EmoInt - a shared task to
	predict the intensity of emotions in tweets. Intensity is a real valued score,
	between 0 and 1. The emotions are classified as - anger, fear, joy and sadness.
	We apply three different deep neural network based models, which approach the
	problem from essentially different directions. Our final performance quantified
	by an average pearson correlation score of 74.7 and an average spearman
	correlation score of 73.5 is obtained using an ensemble of the three models. We
	outperform the baseline model of the shared task by 9.9\% and 9.4\% pearson and
	spearman correlation scores respectively.},
  url       = {http://www.aclweb.org/anthology/W17-5207}
}