@InProceedings{joshi-EtAl:2016:ExProM,
  author    = {Joshi, Aditya  and  Jain, Prayas  and  Bhattacharyya, Pushpak  and  Carman, Mark},
  title     = {‘Who would have thought of that!’: A Hierarchical Topic Model for Extraction of Sarcasm-prevalent Topics and Sarcasm Detection},
  booktitle = {Proceedings of the Workshop on Extra-Propositional Aspects of Meaning in Computational Linguistics (ExProM)},
  month     = {December},
  year      = {2016},
  address   = {Osaka, Japan},
  publisher = {The COLING 2016 Organizing Committee},
  pages     = {1--10},
  abstract  = {Topic Models have been reported to be beneficial for aspect-based sentiment
	analysis. This paper reports the first topic model for sarcasm detection, to
	the best of our knowledge. Designed on the basis of the intuition that
	sarcastic tweets are likely to have a mixture of words of both sentiments as
	against tweets with literal sentiment (either positive or negative), our
	hierarchical topic model discovers sarcasm-prevalent topics and topic-level
	sentiment. Using a dataset of tweets labeled using hashtags, the model
	estimates topic-level, and sentiment-level distributions. Our evaluation shows
	that topics such as `work', `gun laws', `weather' are sarcasm-prevalent topics.
	Our model is also able to discover the mixture of sentiment-bearing words that
	exist in a text of a given sentiment-related label. Finally, we apply our model
	to predict sarcasm in tweets. We outperform two prior work based on statistical
	classifiers with specific features, by around 25%.},
  url       = {http://aclweb.org/anthology/W16-5001}
}