@InProceedings{r-EtAl:2017:WASSA2017,
  author    = {R, Vinayakumar  and  b, premjith  and  s, sachin kumar  and  kp, soman  and  Poornachandran, Prabaharan},
  title     = {deepCybErNet at EmoInt-2017: Deep Emotion Intensities 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     = {259--263},
  abstract  = {This working note presents the methodology used in deepCybErNet submission to
	the shared task on Emotion Intensities in Tweets (EmoInt) WASSA-2017. The goal
	of the task is to predict a real valued score in the range [0-1] for a
	particular tweet with an emotion type. To do this, we used Bag-of-Words and
	embedding based on recurrent network architecture. We have developed two
	systems and experiments are conducted on the Emotion Intensity shared Task 1
	data base at WASSA- 2017. A system which uses word embedding based on recurrent
	network architecture has achieved highest 5 fold cross-validation accuracy.
	This has used embedding with recurrent network to extract optimal features at
	tweet level and logistic regression for prediction. These methods are highly
	language independent and experimental results shows that the proposed methods
	are apt for predicting a real valued score in than range [0-1] for a given
	tweet with its emotion type.},
  url       = {http://www.aclweb.org/anthology/W17-5237}
}