@InProceedings{khanpour-caragea-biyani:2017:I17-2,
  author    = {Khanpour, Hamed  and  Caragea, Cornelia  and  Biyani, Prakhar},
  title     = {Identifying Empathetic Messages in Online Health Communities},
  booktitle = {Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)},
  month     = {November},
  year      = {2017},
  address   = {Taipei, Taiwan},
  publisher = {Asian Federation of Natural Language Processing},
  pages     = {246--251},
  abstract  = {Empathy captures one’s ability to correlate with and understand others’
	emotional states and experiences. Messages with empathetic content are
	considered as one of the main advantages for joining online health communities
	due to their potential to improve people’s moods. Unfortunately, to this
	date, no computational studies exist that automatically identify empathetic
	messages in online health communities. We propose a combination of
	Convolutional Neural Networks (CNN) and Long Short Term Memory (LSTM) networks,
	and show that the proposed model outperforms each individual model (CNN and
	LSTM) as well as several baselines.},
  url       = {http://www.aclweb.org/anthology/I17-2042}
}