Agree or Disagree : Predicting Judgments on Nuanced Assertions

Wojatzki, Michael Maximilian LSF; Zesch, Torsten LSF; Mohammad, Saif M.; Kiritchenko, Svetlana

Being able to predict whether people agree or disagree with an assertion (i.e. an explicit, selfcontained 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. Judgments of groups, however, can be more reliably predicted using a Siamese neural network, which outperforms all other approaches by a wide margin.

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Wojatzki, M.M., Zesch, T., Mohammad, S.M., Kiritchenko, S., 2018. Agree or Disagree: Predicting Judgments on Nuanced Assertions. https://doi.org/10.18653/v1/S18-2026
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