@InProceedings{sasaki-EtAl:2017:Long,
  author    = {Sasaki, Akira  and  Hanawa, Kazuaki  and  Okazaki, Naoaki  and  Inui, Kentaro},
  title     = {Other Topics You May Also Agree or Disagree: Modeling Inter-Topic Preferences using Tweets and Matrix Factorization},
  booktitle = {Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  month     = {July},
  year      = {2017},
  address   = {Vancouver, Canada},
  publisher = {Association for Computational Linguistics},
  pages     = {398--408},
  abstract  = {We presents in this paper our approach for modeling inter-topic preferences of
	Twitter
	users: for example, "those who agree with the Trans-Pacific Partnership (TPP)
	also agree
	with free trade". This kind of knowledge is useful not only for stance
	detection across multiple topics but also for various real-world applications
	including public opinion survey,
	electoral prediction, electoral campaigns, and online debates. In order to
	extract
	users' preferences on Twitter, we design linguistic patterns in which people
	agree
	and disagree about specific topics (e.g., "A is completely wrong'').
	By applying these linguistic patterns to a collection of tweets, we extract
	statements agreeing and disagreeing with various topics. Inspired by previous
	work on
	item recommendation, we formalize the task of modeling inter-topic preferences
	as matrix factorization: representing users' preference as a user-topic matrix
	and mapping both users and topics onto a latent feature space that abstracts
	the preferences. Our experimental results demonstrate both that our presented
	approach is useful in predicting missing preferences of users and that the
	latent vector representations of topics successfully encode inter-topic
	preferences.},
  url       = {http://aclweb.org/anthology/P17-1037}
}