@InProceedings{halder-poddar-kan:2017:WASSA2017,
  author    = {Halder, Kishaloy  and  Poddar, Lahari  and  Kan, Min-Yen},
  title     = {Modeling Temporal Progression of Emotional Status in Mental Health Forum: A Recurrent Neural Net Approach},
  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     = {127--135},
  abstract  = {Patients turn to Online Health Communities not only for information on specific
	conditions but also for emotional support. Previous research has indicated that
	the progression of emotional status can be studied through the linguistic
	patterns of an individual's posts.  We analyze a real-world dataset from the
	Mental Health section of HealthBoards.com. Estimated from the word usages in
	their posts, we find that the emotional progress across patients vary widely.
	We study the problem of predicting a patient's emotional status in the future
	from her past posts and we propose a Recurrent Neural Network (RNN) based
	architecture to address it.  We find that the future emotional status can be
	predicted with reasonable accuracy given her historical posts and participation
	features. Our evaluation results demonstrate the efficacy of our proposed
	architecture, by outperforming state-of-the-art approaches with over 0.13
	reduction in Mean Absolute Error.},
  url       = {http://www.aclweb.org/anthology/W17-5217}
}