@InProceedings{wojatzki-EtAl:2018:S18-2,
  author    = {Wojatzki, Michael  and  Zesch, Torsten  and  Mohammad, Saif  and  Kiritchenko, Svetlana},
  title     = {Agree or Disagree: Predicting Judgments on Nuanced Assertions},
  booktitle = {Proceedings of the Seventh Joint Conference on Lexical and Computational Semantics},
  month     = {June},
  year      = {2018},
  address   = {New Orleans, Louisiana},
  publisher = {Association for Computational Linguistics},
  pages     = {214--224},
  abstract  = {Being able to predict whether people agree or disagree with an assertion (i.e. an explicit, self-contained statement) has several applications ranging from predicting how many people will like or dislike a social media post to classifying posts based on whether they are in accordance with a particular point of view. We formalize this as two NLP tasks: predicting judgments of (i) individuals and (ii) groups based on the text of the assertion and previous judgments. We evaluate a wide range of approaches on a crowdsourced data set containing over 100,000 judgments on over 2,000 assertions. We find that predicting individual judgments is a hard task with our best results only slightly exceeding a majority baseline, but that judgments of groups can be more reliably predicted using a Siamese neural network, which outperforms all other approaches by a wide margin.},
  url       = {http://www.aclweb.org/anthology/S18-2026}
}