@InProceedings{swanberg-EtAl:2018:S18-1,
  author    = {Swanberg, Kevin  and  Mirza, Madiha  and  Pedersen, Ted  and  Wang, Zhenduo},
  title     = {ALANIS at SemEval-2018 Task 3: A Feature Engineering Approach to Irony Detection in English Tweets},
  booktitle = {Proceedings of The 12th International Workshop on Semantic Evaluation},
  month     = {June},
  year      = {2018},
  address   = {New Orleans, Louisiana},
  publisher = {Association for Computational Linguistics},
  pages     = {507--511},
  abstract  = {This paper describes the ALANIS system that participated in Task 3 of SemEval-2018. We develop a system for detection of irony, as well as the detection of three types of irony: verbal polar irony, other verbal irony, and situational irony. The system uses a logistic regression model in subtask A and a voted classifier system with manually developed features to identify ironic tweets. This model improves on a naive bayes baseline by about 8 percent on training set.},
  url       = {http://www.aclweb.org/anthology/S18-1082}
}