@InProceedings{huang-EtAl:2017:EMNLP2017Demos,
  author    = {Huang, Chieh-Yang  and  Labetoulle, Tristan  and  Huang, Ting-Hao  and  Chen, Yi-Pei  and  Chen, Hung-Chen  and  Srivastava, Vallari  and  Ku, Lun-Wei},
  title     = {MoodSwipe: A Soft Keyboard that Suggests MessageBased on User-Specified Emotions},
  booktitle = {Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing: System Demonstrations},
  month     = {September},
  year      = {2017},
  address   = {Copenhagen, Denmark},
  publisher = {Association for Computational Linguistics},
  pages     = {73--78},
  abstract  = {We present MoodSwipe, a soft keyboard that suggests text messages given the
	user-specified emotions utilizing the real dialog data. The aim of MoodSwipe is
	to create a convenient user interface to enjoy the technology of emotion
	classification and text suggestion, and at the same time to collect labeled
	data automatically for developing more advanced technologies.
	While users select the MoodSwipe keyboard, they can type as usual but sense the
	emotion conveyed by their text and receive suggestions for their message as a
	benefit. In MoodSwipe, the detected emotions serve as the medium for suggested
	texts, where viewing the latter is the incentive to correcting the former. We
	conduct several experiments to show the superiority of the emotion
	classification models trained on the dialog data, and further to verify good
	emotion cues are important context for text suggestion.},
  url       = {http://www.aclweb.org/anthology/D17-2013}
}