@InProceedings{ebrahimi-dou-lowd:2016:COLING,
  author    = {Ebrahimi, Javid  and  Dou, Dejing  and  Lowd, Daniel},
  title     = {A Joint Sentiment-Target-Stance Model for Stance Classification in Tweets},
  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     = {2656--2665},
  abstract  = {Classifying the stance expressed in online microblogging social media is an
	emerging problem in opinion mining. We propose a probabilistic approach to
	stance classification in tweets, which models stance, target of stance, and
	sentiment of tweet, jointly. Instead of simply conjoining the sentiment or
	target variables as extra variables to the feature space, we use a novel
	formulation to incorporate three-way interactions among sentiment-stance-input
	variables and three-way interactions among target-stance-input variables.
	The proposed specification intuitively aims to discriminate sentiment features
	from target features for stance classification.
	In addition, regularizing a single stance classifier, which handles all
	targets, acts as a soft weight-sharing among them. We demonstrate that
	discriminative training of this model achieves the state-of-the-art results in
	supervised stance classification, and its generative training obtains
	competitive results in the weakly supervised setting.},
  url       = {http://aclweb.org/anthology/C16-1250}
}