@InProceedings{joshi:2017:WASSA2017,
  author    = {Joshi, Aditya},
  title     = {Detecting Sarcasm Using Different Forms Of Incongruity},
  booktitle = {Proceedings of the 8th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis},
  month     = {September},
  year      = {2017},
  address   = {Copenhagen, Denmark},
  publisher = {Association for Computational Linguistics},
  pages     = {1},
  abstract  = {Sarcasm is a form of verbal irony that is intended to express contempt or
	ridicule. Often quoted as a challenge to sentiment analysis, sarcasm involves
	use of words of positive or no polarity to convey negative sentiment.
	Incongruity has been observed to be at the heart of sarcasm understanding in
	humans. Our work in sarcasm detection identifies different forms of incongruity
	and employs different machine learning techniques to capture them. This talk
	will describe the approach, datasets and challenges in sarcasm detection using
	different forms of incongruity. 
	We identify two forms of incongruity: incongruity which can be understood based
	on the target text and common background knowledge, and incongruity which can
	be understood based on the target text and additional, specific context. The
	former involves use of sentiment-based features, word embeddings, and topic
	models. The latter involves creation of author's historical context based on
	their historical data, and creation of conversational context for sarcasm
	detection of dialogue.},
  url       = {http://www.aclweb.org/anthology/W17-5201}
}