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Open Secrets and Wrong Rights: Automatic Satire Detection in English Text

Authors:

Aishwarya Reganti,

Tushar Maheshwari,

Amitava Das,

Erik CambriaAuthors Info & Claims

CSCW '17 Companion: Companion of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing

Pages 291 - 294

https://doi.org/10.1145/3022198.3026344

Published: 25 February 2017 Publication History

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Abstract

Satire is an element of figurative language which often conveys feelings contrary to what is literally stated. It refers to a trenchant wit, irony, or sarcasm used to expose discredit vice or folly. The presence of a satirical utterance in text can entirely change the sentiment of the statement, hence it is necessary to distinguish between true positive statements and satirical ones.In this paper, we identify key value components and features for automatic satire detection. Our experiments have been carried out on three data sets, namely, tweets, product reviews and newswire articles. We examine the impact of a number of state of the art features as well as new generalised textual features.

References

[1]

Clint Burfoot and Timothy Baldwin. 2009. Automatic Satire Detection: Are You Having a Laugh?. In Proceedings of the ACL-IJCNLP 2009 Conference Short Papers (ACLShort '09). Association for Computational Linguistics, Stroudsburg, PA, USA, 161--164.

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Andrea Esuli and Fabrizio Sebastiani. 2006. Sentiwordnet: A publicly available lexical resource for opinion mining. In Proceedings of LREC, Vol. 6. Citeseer, 417--422.

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Elena Filatova. 2012. Irony and Sarcasm: Corpus Generation and Analysis Using Crowdsourcing. In Proceedings of the Eighth International Conference on Language Resources and Evaluation (LREC-2012), Istanbul, Turkey, May 23-25, 2012. 392--398.

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Saif M Mohammad and Peter D Turney. 2013. Crowdsourcing a word-emotion association lexicon. Computational Intelligence 29, 3 (2013), 436--465.

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Soujanya Poria, Erik Cambria, Devamanyu Hazarika, and Prateek Vij. 2016. A deeper look into sarcastic tweets using deep convolutional neural networks. arXiv preprint arXiv:1610.08815 (2016).

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Cited By

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Katyayan PJoshi N(2020)Sarcasm Detection Algorithms Based on Sentiment StrengthIntelligent Data Analysis10.1002/9781119544487.ch14(289-306)Online publication date: 2-Jun-2020
https://doi.org/10.1002/9781119544487.ch14
Shrawankar UChandankhede C(2019)Sarcasm Detection for Workplace Stress ManagementInternational Journal of Synthetic Emotions10.4018/IJSE.201907010110:2(1-17)Online publication date: 1-Jul-2019
https://doi.org/10.4018/IJSE.2019070101
Katyayan PJoshi N(2019)Sarcasm Detection Approaches for English LanguageSmart Techniques for a Smarter Planet10.1007/978-3-030-03131-2_9(167-183)Online publication date: 30-Jan-2019
https://doi.org/10.1007/978-3-030-03131-2_9

Index Terms

Open Secrets and Wrong Rights: Automatic Satire Detection in English Text
1. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing

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Published In

CSCW '17 Companion: Companion of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing

February 2017

472 pages

ISBN:9781450346887

DOI:10.1145/3022198

General Chairs:
Charlotte P. Lee
University of Washington
,
Steve Poltrock
Retired
,
Program Chairs:
Louise Barkhuus
Stockholm University and Cornell Tech
,
Marcos Borges
Universidade Federal do Rio de Janeiro
,
Wendy Kellogg
IBM T.J. Watson Research Center

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

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Published: 25 February 2017

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CSCW '17

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SIGCHI

CSCW '17: Computer Supported Cooperative Work and Social Computing

February 25 - March 1, 2017

Oregon, Portland, USA

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CSCW '17 Companion Paper Acceptance Rate 183 of 530 submissions, 35%;

Overall Acceptance Rate 2,235 of 8,521 submissions, 26%

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CSCW '25

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October 18 - 22, 2025

Bergen , Norway

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Cited By

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Katyayan PJoshi N(2020)Sarcasm Detection Algorithms Based on Sentiment StrengthIntelligent Data Analysis10.1002/9781119544487.ch14(289-306)Online publication date: 2-Jun-2020
https://doi.org/10.1002/9781119544487.ch14
Shrawankar UChandankhede C(2019)Sarcasm Detection for Workplace Stress ManagementInternational Journal of Synthetic Emotions10.4018/IJSE.201907010110:2(1-17)Online publication date: 1-Jul-2019
https://doi.org/10.4018/IJSE.2019070101
Katyayan PJoshi N(2019)Sarcasm Detection Approaches for English LanguageSmart Techniques for a Smarter Planet10.1007/978-3-030-03131-2_9(167-183)Online publication date: 30-Jan-2019
https://doi.org/10.1007/978-3-030-03131-2_9

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Figurative messages and affect in Twitter

Satire or Fake News: Social Media Consumers' Socio-Demographics Decide