@InProceedings{gupta-yang:2017:SemEval,
  author    = {Gupta, Raj Kumar  and  Yang, Yinping},
  title     = {CrystalNest at SemEval-2017 Task 4: Using Sarcasm Detection for Enhancing Sentiment Classification and Quantification},
  booktitle = {Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)},
  month     = {August},
  year      = {2017},
  address   = {Vancouver, Canada},
  publisher = {Association for Computational Linguistics},
  pages     = {626--633},
  abstract  = {This paper describes a system developed for a shared sentiment analysis task
	and its subtasks organized by SemEval-2017. A key feature of our system is the
	embedded ability to detect sarcasm in order to enhance the performance of
	sentiment classification. We first constructed an
	affect-cognition-sociolinguistics sarcasm features model and trained a
	SVM-based classifier for detecting sarcastic expressions from general tweets.
	For sentiment prediction, we developed CrystalNest-- a two-level cascade
	classification system using features combining sarcasm score derived from our
	sarcasm classifier, sentiment scores from Alchemy, NRC lexicon, n-grams, word
	embedding vectors, and part-of-speech features. We found that the sarcasm
	detection derived features consistently benefited key sentiment analysis
	evaluation metrics, in different degrees, across four subtasks A-D.},
  url       = {http://www.aclweb.org/anthology/S17-2103}
}