Signaling sarcasm: From hyperbole to hashtag

Irony Detection in Twitter

“…This is mainly due to availability of a large set of samples of ironic texts, which are easy to be collected relying on the behavior of Twitter users, who often explicitly mark their ironic messages by using hashtags such as '#irony' or '#sarcasm'. The pretty good reliability of the user-generated hashtags as golden labels for irony has been experimentally confirmed by [Kunneman et al 2015]. Moreover, it seems that, due to the interaction model underlying the microblogging platform, irony expressed here could be somehow easier to analyze.…”

“…Furthermore, there are some efforts in other so-cial media such as customer reviews from Amazon 1 [Filatova 2012;Buschmeier et al 2014]; comments from the online debate sites such as 4forums.com 2 [Abbott et al 2011;Lukin and Walker 2013] and, recently, Reddit 3 . The majority of the research in irony detection has been addressed in English, although there is some research in other languages, such as: Dutch [Kunneman et al 2015], Italian [Bosco et al 2013], Czech [Ptáček et al 2014], French [Karoui et al 2015], Portuguese [Carvalho et al 2009] and Chinese [Tang and Chen 2014]. A shared task for English on sentiment analysis of figurative language in Twitter has been organized at SemEval-2015 for the first time [Ghosh et al 2015], and a pilot shared task for Italian on irony detection has been proposed in Sentipolc-2014 within the periodic evaluation campaign EVALITA [Basile et al 2014;Attardi et al 2015].…”

Exploring the fine-grained analysis and automatic detection of irony on Twitter

“…Also, it was observed that harsh tweets contained more secondperson pronouns (1.18% of all tokens) than non-harsh tweets (0.59%). As shown by among others Kunneman et al (2015) and Riloff et al (2013), ironic utterances are likely to contain markers such as interjections (e.g. 'yeah right') or intensifiers and diminishers to express hyperbole and understatements.…”

“…They distinguished tweets with the #irony hashtag from tweets with the hashtags #education, #humor or #politics and found that the best scores were obtained by distinguishing #irony from #humor tweets. Kunneman et al (2015) pioneered irony detection in Dutch tweets using word n-gram features and a Balanced Winnow classifier. They trained a classifier by contrasting the hashtag-labelled irony tweets (i.e.…”

“…#irony, #sarcasm, #not) assigned by the author of the text to label instances in an irony corpus, but this has shown to introduce noise into the labelled training data (Kunneman et al 2015). For the current study, we also collected ironic tweets using the above hashtags, but supplemented them with manual annotations using a finegrained annotation scheme (Van Hee et al 2016b).…”