@InProceedings{abdulmageed-ungar:2017:Long,
  author    = {Abdul-Mageed, Muhammad  and  Ungar, Lyle},
  title     = {EmoNet: Fine-Grained Emotion Detection with Gated Recurrent Neural Networks},
  booktitle = {Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  month     = {July},
  year      = {2017},
  address   = {Vancouver, Canada},
  publisher = {Association for Computational Linguistics},
  pages     = {718--728},
  abstract  = {Accurate detection of emotion from natural language has applications ranging
	from building emotional chatbots to better understanding individuals and their
	lives. However, progress on emotion detection has been hampered by the absence
	of large labeled datasets.  In this work, we build a very large dataset for
	fine-grained emotions and develop deep learning models on it. We achieve a new
	state-of-the-art on 24 fine-grained types of emotions (with an average accuracy
	of 87.58%). We also extend the task beyond emotion types to model Robert
	Plutick's 8 primary emotion dimensions, acquiring a superior accuracy of
	95.68%.},
  url       = {http://aclweb.org/anthology/P17-1067}
}