@inproceedings{swanberg-etal-2018-alanis,
    title = "{ALANIS} at {S}em{E}val-2018 Task 3: A Feature Engineering Approach to Irony Detection in {E}nglish Tweets",
    author = "Swanberg, Kevin  and
      Mirza, Madiha  and
      Pedersen, Ted  and
      Wang, Zhenduo",
    editor = "Apidianaki, Marianna  and
      Mohammad, Saif M.  and
      May, Jonathan  and
      Shutova, Ekaterina  and
      Bethard, Steven  and
      Carpuat, Marine",
    booktitle = "Proceedings of the 12th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest_wac_2008/S18-1082/",
    doi = "10.18653/v1/S18-1082",
    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."
}