@InProceedings{chen-ku:2016:COLING,
  author    = {Chen, Wei-Fan  and  Ku, Lun-Wei},
  title     = {UTCNN: a Deep Learning Model of Stance Classification on Social Media Text},
  booktitle = {Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers},
  month     = {December},
  year      = {2016},
  address   = {Osaka, Japan},
  publisher = {The COLING 2016 Organizing Committee},
  pages     = {1635--1645},
  abstract  = {Most neural network models for document classification on social media focus on
	text information to the neglect of other information on these platforms. In
	this paper, we classify post stance on social media channels and develop UTCNN,
	a neural network model that incorporates user tastes, topic tastes, and user
	comments on posts. UTCNN not only works on social media texts, but also
	analyzes texts in forums and message boards. Experiments performed on Chinese
	Facebook data and English online debate forum data show that UTCNN achieves a
	0.755 macro average f-score for supportive, neutral, and unsupportive stance
	classes on Facebook data, which is significantly better than models in which
	either user, topic, or comment information is withheld. This model design
	greatly mitigates the lack of data for the minor class. In addition, UTCNN
	yields a 0.842 accuracy on English online debate forum data, which also
	significantly outperforms results from previous work, showing that UTCNN
	performs well regardless of language or platform.},
  url       = {http://aclweb.org/anthology/C16-1154}
}