@InProceedings{poria-EtAl:2016:COLING,
  author    = {Poria, Soujanya  and  Cambria, Erik  and  Hazarika, Devamanyu  and  Vij, Prateek},
  title     = {A Deeper Look into Sarcastic Tweets Using Deep Convolutional Neural Networks},
  booktitle = {Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers},
  month     = {December},
  year      = {2016},
  address   = {Osaka, Japan},
  publisher = {The COLING 2016 Organizing Committee},
  pages     = {1601--1612},
  abstract  = {Sarcasm detection is a key task for many natural language processing tasks. In
	sentiment analysis, for example, sarcasm can flip the polarity of an
	“apparently positive” sentence and, hence, negatively affect polarity
	detection performance. To date, most approaches to sarcasm detection have
	treated the task primarily as a text categorization problem. Sarcasm, however,
	can be expressed in very subtle ways and requires a deeper understanding of
	natural language that standard text categorization techniques cannot grasp. In
	this work, we develop models based on a pre-trained convolutional neural
	network for extracting sentiment, emotion and personality features for sarcasm
	detection. Such features, along with the network’s baseline features, allow
	the proposed models to outperform the state of the art on benchmark datasets.
	We also address the often ignored generalizability issue of classifying data
	that have not been seen by the models at learning phase.},
  url       = {http://aclweb.org/anthology/C16-1151}
}