article

A multidimensional approach for detecting irony in Twitter

Authors:

Tony VealeAuthors Info & Claims

Language Resources and Evaluation, Volume 47, Issue 1

Pages 239 - 268

https://doi.org/10.1007/s10579-012-9196-x

Published: 01 March 2013 Publication History

Abstract

Irony is a pervasive aspect of many online texts, one made all the more difficult by the absence of face-to-face contact and vocal intonation. As our media increasingly become more social , the problem of irony detection will become even more pressing. We describe here a set of textual features for recognizing irony at a linguistic level, especially in short texts created via social media such as Twitter postings or "tweets". Our experiments concern four freely available data sets that were retrieved from Twitter using content words (e.g. "Toyota") and user-generated tags (e.g. "#irony"). We construct a new model of irony detection that is assessed along two dimensions: representativeness and relevance. Initial results are largely positive, and provide valuable insights into the figurative issues facing tasks such as sentiment analysis, assessment of online reputations, or decision making.

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Cited By

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Maladry ALefever EVan Hee CHoste V(2024)The limitations of irony detection in Dutch social mediaLanguage Resources and Evaluation10.1007/s10579-023-09656-158:4(1355-1386)Online publication date: 1-Dec-2024
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cover image Language Resources and Evaluation

Language Resources and Evaluation Volume 47, Issue 1

March 2013

263 pages

ISSN:1574-020X

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Copyright © Copyright © 2013 Springer Science+Business Media Dordrecht.

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Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 01 March 2013

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Cited By

Li YLi YZhang SLiu GChen YShang RJiao L(2024)An attention-based, context-aware multimodal fusion method for sarcasm detection using inter-modality inconsistencyKnowledge-Based Systems10.1016/j.knosys.2024.111457287:COnline publication date: 5-Mar-2024
https://dl.acm.org/doi/10.1016/j.knosys.2024.111457
Maladry ALefever EVan Hee CHoste V(2024)The limitations of irony detection in Dutch social mediaLanguage Resources and Evaluation10.1007/s10579-023-09656-158:4(1355-1386)Online publication date: 1-Dec-2024
https://dl.acm.org/doi/10.1007/s10579-023-09656-1
Fatima EKanwal HKhan JKhan N(2024)An exploratory and automated study of sarcasm detection and classification in app stores using fine-tuned deep learning classifiersAutomated Software Engineering10.1007/s10515-024-00468-331:2Online publication date: 27-Aug-2024
https://dl.acm.org/doi/10.1007/s10515-024-00468-3
Tian HMa XBardzell JPatil S(2022)Non-literal Communication in Chinese Internet Spaces: A Case Study of FishingProceedings of the ACM on Human-Computer Interaction10.1145/35129516:CSCW1(1-32)Online publication date: 7-Apr-2022
https://dl.acm.org/doi/10.1145/3512951
Das SGhosh SKolya AEkbal A(2022)Unparalleled sarcasm: a framework of parallel deep LSTMs with cross activation functions towards detection and generation of sarcastic statementsLanguage Resources and Evaluation10.1007/s10579-022-09622-357:2(765-802)Online publication date: 2-Oct-2022
https://dl.acm.org/doi/10.1007/s10579-022-09622-3
Qiu X(2022)Irony Recognition in Chinese Text Based on Linguistic Features and Attention MechanismChinese Lexical Semantics10.1007/978-3-031-28956-9_28(351-363)Online publication date: 14-May-2022
https://dl.acm.org/doi/10.1007/978-3-031-28956-9_28
Kumar ADikshit SAlbuquerque V(2021)Explainable Artificial Intelligence for Sarcasm Detection in DialoguesWireless Communications & Mobile Computing10.1155/2021/29393342021Online publication date: 1-Jan-2021
https://dl.acm.org/doi/10.1155/2021/2939334
Cortis KDavis B(2021)Over a decade of social opinion mining: a systematic reviewArtificial Intelligence Review10.1007/s10462-021-10030-254:7(4873-4965)Online publication date: 1-Oct-2021
https://dl.acm.org/doi/10.1007/s10462-021-10030-2
Reddy SAgarwal S(2021)Isn’t It Ironic, Don’t You Think?Neural Information Processing10.1007/978-3-030-92273-3_43(526-536)Online publication date: 8-Dec-2021
https://dl.acm.org/doi/10.1007/978-3-030-92273-3_43
Famiglini LFersini ERosso P(2021)On the Generalization of Figurative Language Detection: The Case of Irony and SarcasmNatural Language Processing and Information Systems10.1007/978-3-030-80599-9_16(178-186)Online publication date: 23-Jun-2021
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