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      iSarcasm: A Dataset of Intended Sarcasm

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          Abstract

          This paper considers the distinction between intended and perceived sarcasm in the context of textual sarcasm detection. The former occurs when an utterance is sarcastic from the perspective of its author, while the latter occurs when the utterance is interpreted as sarcastic by the audience. We show the limitations of previous labelling methods in capturing intended sarcasm and introduce the iSarcasm dataset of tweets labeled for sarcasm directly by their authors. We experiment with sarcasm detection models on our dataset. The low performance indicates that sarcasm might be a phenomenon under-studied computationally thus far.

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          Why not say it directly? The social functions of irony

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            Making objective decisions from subjective data: Detecting irony in customer reviews

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              Test of the mention theory of irony.

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                Author and article information

                Journal
                08 November 2019
                Article
                1911.03123
                14f9a492-0ed8-41f0-a30c-7dec0e94e2be

                http://creativecommons.org/licenses/by/4.0/

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                Custom metadata
                10 pages
                cs.CL

                Theoretical computer science
                Theoretical computer science

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