@InProceedings{ghosh-veale:2017:EMNLP2017,
  author    = {Ghosh, Aniruddha  and  Veale, Tony},
  title     = {Magnets for Sarcasm: Making Sarcasm Detection Timely, Contextual and Very Personal},
  booktitle = {Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing},
  month     = {September},
  year      = {2017},
  address   = {Copenhagen, Denmark},
  publisher = {Association for Computational Linguistics},
  pages     = {482--491},
  abstract  = {Sarcasm is a pervasive phenomenon in social media, permitting the concise
	communication of meaning, affect and attitude. Concision requires wit to
	produce and wit to understand, which demands from each party knowledge of
	norms, context and a speaker's mindset. Insight into a speaker's psychological
	profile at the time of production is a valuable source of context for sarcasm
	detection. Using a neural architecture, we show significant gains in detection
	accuracy when knowledge of the speaker's mood at the time of production can be
	inferred. Our focus is on sarcasm detection on Twitter, and show that the mood
	exhibited by a speaker over tweets leading up to a new post is as useful a cue
	for sarcasm as the topical context of the post itself. The work opens the door
	to an empirical exploration not just of sarcasm in text but of the sarcastic
	state of mind.},
  url       = {https://www.aclweb.org/anthology/D17-1050}
}