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In this work, we address the problem of detecting irony in tweets, casting it as a classification problem. We propose a novel model that explores the use of affective features based on a wide range of lexical resources available for English, reflecting different facets of affect. Classification experiments over different corpora show that affective information helps in distinguishing among ironic and nonironic tweets. Our model outperforms the state of the art in almost all cases.<\/jats:p>","DOI":"10.1145\/2930663","type":"journal-article","created":{"date-parts":[[2016,7,11]],"date-time":"2016-07-11T12:17:07Z","timestamp":1468239427000},"page":"1-24","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":77,"title":["Irony Detection in Twitter"],"prefix":"10.1145","volume":"16","author":[{"given":"Delia Iraz\u00fa Herna\u0144dez","family":"Far\u00edas","sequence":"first","affiliation":[{"name":"PRHLT Research Center, Universitat Polit\u00e8cnica de Val\u00e8ncia; Dipartimento di Informatica, University of Turin"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Viviana","family":"Patti","sequence":"additional","affiliation":[{"name":"Dipartimento di Informatica, University of Turin, Turin, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Paolo","family":"Rosso","sequence":"additional","affiliation":[{"name":"PRHLT Research Center, Universitat Polit\u00e8cnica de Val\u00e8ncia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2016,7,9]]},"reference":[{"key":"e_1_2_1_1_1","volume-title":"Proceedings of the Workshop on Languages in Social Media (LSM\u201911)","author":"Abbott Rob","year":"2011","unstructured":"Rob Abbott , Marilyn Walker , Pranav Anand , Jean E. 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