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A Survey of Figurative Language and Its Computational Detection in Online Social Networks

Authors:

Muhammad Abulaish,

Mohammed J. ZakiAuthors Info & Claims

ACM Transactions on the Web (TWEB), Volume 14, Issue 1

Article No.: 3, Pages 1 - 52

https://doi.org/10.1145/3375547

Published: 07 February 2020 Publication History

Editorial Notes

The authors have requested minor, non-substantive changes to the VoR and, in accordance with ACM policies, a Corrected VoR was published on October 19, 2020. For reference purposes the VoR may still be accessed via the Supplemental Material section on this page.

Abstract

The frequent usage of figurative language on online social networks, especially on Twitter, has the potential to mislead traditional sentiment analysis and recommender systems. Due to the extensive use of slangs, bashes, flames, and non-literal texts, tweets are a great source of figurative language, such as sarcasm, irony, metaphor, simile, hyperbole, humor, and satire. Starting with a brief introduction of figurative language and its various categories, this article presents an in-depth survey of the state-of-the-art techniques for computational detection of seven different figurative language categories, mainly on Twitter. For each figurative language category, we present details about the characterizing features, datasets, and state-of-the-art computational detection approaches. Finally, we discuss open challenges and future directions of research for each figurative language category.

Supplementary Material

3375547-VoR (3375547-vor.pdf)

Version of Record for "A Survey of Figurative Language and Its Computational Detection in Online Social Networks" by Abulaish et al., ACM Transactions on the Web, Volume 14, Issue 1 (TWEB 14:1).

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References

[1]

Gavin Abercrombie and Dirk Hovy. 2016. Putting sarcasm detection into context: The effects of class imbalance and manual labelling on supervised machine classification of Twitter conversations. In Proceedings of the 54th Meeting of the Association for Computational Linguistics--Student Research Workshop (ACL-SRW’16). ACL, 107--113.

[2]

Muhammad Abulaish and Ashraf Kamal. 2018. Self-deprecating sarcasm detection: An amalgamation of rule-based and machine learning approach. In Proceedings of the IEEE/WIC/ACM International Conference on Web Intelligence (WI’18). IEEE, 574--579.

[3]

Ameeta Agrawal and Aijun An. 2018. Affective representations for sarcasm detection. In Proceedings of the 41st International ACM SIGIR Conference on Research 8 Development in Information Retrieval (SIGIR’18). ACM, 1029--1032.

Digital Library

[4]

Yasemin Altun, Ioannis Tsochantaridis, and Thomas Hofmann. 2003. Hidden Markov support vector machines. In Proceedings of the 20th International Conference on Machine Learning (ICML’03). AAAI, 3--10.

Digital Library

[5]

Silvio Amir, Byron C. Wallace, Hao Lyu, Paula Carvalho, and Mário J. Silva. 2016. Modelling context with user embeddings for sarcasm detection in social media. In Proceedings of the 20th Special Interest Group on Natural Language Learning Conference on Computational Natural Language Learning (SIGNLL-CoNLL’16). ACL, 167--177.

[6]

Salvatore Attardo. 2010. Linguistic Theories of Humor. Vol. 1. Walter de Gruyter.

[7]

Salvatore Attardo and Victor Raskin. 1991. Script theory revis(it)ed: Joke similarity and joke representation model. Humor-Int. J. Humor Res. 4, 3--4 (1991), 293--348.

[8]

David Bamman and Noah A. Smith. 2015. Contextualized sarcasm detection on Twitter. In Proceedings of the 9th International Association for the Advancement of Artificial Intelligence Conference on Web and Social Media (ICWSM’15). Citeseer, 574--577.

[9]

Francesco Barbieri, Francesco Ronzano, and Horacio Saggion. 2015. Do we criticise (and laugh) in the same way? Automatic detection of multi-lingual satirical news in Twitter. In Proceedings of the 24th International Joint Conference on Artificial Intelligence (IJCAI’15). 1215--1221.

Digital Library

[10]

Francesco Barbieri, Francesco Ronzano, and Horacio Saggion. 2015. Is this tweet satirical? A computational approach for satire detection in Spanish. Proc. Lenguaje Nat. 55 (2015), 135--142.

[11]

Francesco Barbieri, Francesco Ronzano, and Horacio Saggion. 2015. UPF-taln: Semeval 2015 tasks 10 and 11 sentiment analysis of literal and figurative language in Twitter. In Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval’15). ACL, 704--708.

[12]

Francesco Barbieri and Horacio Saggion. 2014. Automatic detection of irony and humour in Twitter. In Proceedings of the 5th International Conference on Computational Creativity (ICCC’14).

[13]

Francesco Barbieri and Horacio Saggion. 2014. Modelling irony in Twitter. In Proceedings of the Student Research Workshop at the 14th Conference of the European Chapter of the Association for Computational Linguistics (SRW-EACL’14). ACL, 56--64.

[14]

Francesco Barbieri and Horacio Saggion. 2014. Modelling irony in Twitter: Feature analysis and evaluation. In Proceedings of the 9th International Conference on Language Resources and Evaluation (LREC’14). European Language Resources Association (ELRA), 4258--4264.

[15]

Francesco Barbieri, Horacio Saggion, and Francesco Ronzano. 2014. Modelling sarcasm in Twitter, a novel approach. In Proceedings of the 5th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis (WASSA-ACL’14). ACL, 50--58.

[16]

Valerio Basile, Andrea Bolioli, Malvina Nissim, Viviana Patti, and Paolo Rosso. 2014. Overview of the Evalita 2014 sentiment polarity classification task. In Proceedings of the 4th Evaluation Campaign of Natural Language Processing and Speech tools for Italian (EVALITA’14). 50--57.

[17]

Monroe C. Beardsley. 1981. Aesthetics, Problems in the Philosophy of Criticism (2nd ed.). Hackett Publishing Company, Inc., Indianapolis, IN.

[18]

Sven V. D. Beukel and Lora Aroyo. 2018. Homonym detection for humor recognition in short text. In Proceedings of the 9th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis (WASSA-ACL’18). ACL, 286--291.

[19]

Santosh Kumar Bharti, Korra Sathya Babu, and Sanjay Kumar Jena. 2017. Harnessing online news for sarcasm detection in Hindi tweets. In Proceedings of the 7th International Conference on Pattern Recognition and Machine Intelligence (PReMI’17), B. Uma Shankar, Kuntal Ghosh, Deba Prasad Mandal, Shubhra Sankar Ray, David Zhang, and Sankar K. Pal (Eds.). Springer, 679--686.

[20]

Santosh Kumar Bharti, Korra Sathya Babu, and Sanjay Kumar Jena Jena. 2015. Parsing-based sarcasm sentiment recognition in Twitter data. In Proceedings of the IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM’15). IEEE, 1373--1380.

Digital Library

[21]

Santosh Kumar Bharti, Bakhtyar Vachha, Ramkrushna Pradhan, Korra Sathya Babu, and Sanjay Kumar Jena. 2016. Sarcastic sentiment detection in tweets streamed in real time: A big data approach. Dig. Commun. Netw. 2, 3 (July 2016), 108--121.

[22]

Cristina Bosco, Viviana Patti, and Andrea Bolioli. 2013. Developing corpora for sentiment analysis: The case of irony and Senti-TUT. In Proceedings of the 24th International Joint Conference on Artificial Intelligence (IJCAI’13). 4158--4162.

Digital Library

[23]

Mondher Bouazizi and Tomoaki Ohtsuki. 2015. Opinion mining in Twitter how to make use of sarcasm to enhance sentiment analysis. In Proceedings of the IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM’15). IEEE, 1594--1597.

Digital Library

[24]

Mondher Bouazizi and Tomoaki Ohtsuki. 2016. A pattern-based approach for sarcasm detection on Twitter. IEEE Access 4 (Sept. 2016), 5477--5488.

[25]

Andrea Bowes and Albert Katz. 2011. When sarcasm stings. Disc. Proc. 48, 4 (2011), 215--236.

[26]

David B. Bracewell, Marc T. Tomlinson, and Michael Mohler. 2013. Determining the conceptual space of metaphoric expressions. In Proceedings of the 14th International Conference on Intelligent Text Processing and Computational Linguistics (CICLing’13). Springer, 487--500.

Digital Library

[27]

Jane Bromley, Isabelle Guyon, Yann LeCun, Eduard Säckinger, and Roopak Shah. 1994. Signature verification using a “siamese” time delay neural network. In Proceedings of the Conference on Advances in Neural Information Processing Systems (NIPS’94). 737--744.

[28]

Marc Brysbaert, Amy B. Warriner, and Victor Kuperman. 2014. Concreteness ratings for 40 thousand generally known English word lemmas. Behav. Res. Meth. 46, 3 (2014), 904--911.

[29]

Clint Burfoot and Timothy Baldwin. 2009. Automatic satire detection: Are you having a laugh? In Proceedings of the Association for Computational Linguistics and International Joint Conference on Natural Language Processing (ACL-IJCNLP’09). ACL and AFNLP, 161--164.

[30]

Arnie Cann, Lawrence G. Calhoun, and Janet S. Banks. 1997. On the role of humor appreciation in interpersonal attraction: It’s no joking matter. Humor-Int. J. Humor Res. 10, 1 (1997), 77--90.

[31]

Paula Carvalho, Luís Sarmento, Mário J. Silva, and Eugénio De Oliveira. 2009. Clues for detecting irony in user-generated contents: Oh...!! It’s so easy;-). In Proceedings of the 1st International Workshop on Topic-sentiment Analysis for Mass Opinion (TSA’09). ACM, 53--56.

Digital Library

[32]

Santiago Castro, Luis Chiruzzo, and Aiala Rosá. 2018. Overview of the HAHA task: Humor analysis based on human annotation at IberEval 2018. In Proceedings of the IberEval Workshop. 187--194.

[33]

Chih-Chung Chang and Chih-Jen Lin. 2011. LIBSVM: A library for support vector machines. ACM Trans. Intell. Syst. Technol. 2, 3 (2011), 27.

Digital Library

[34]

Basilis Charalampakis, Dimitris Spathis, Elias Kouslis, and Katia Kermanidis. 2016. A comparison between semi-supervised and supervised text mining techniques on detecting irony in Greek political tweets. Eng. Appl. Artif. Intell. 51 (Mar. 2016), 50--57.

[35]

Peng-Yu Chen and Von-Wun Soo. 2018. Humor recognition using deep learning. In Proceedings of the 16th Conference on the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 2: Short Papers), (NAACL-HLT’18). ACL, 113--117.

[36]

Herbert H. Clark and Richard J. Gerrig. 1984. On the pretense theory of irony. J. Exper. Psychol.: Gen. 113, 1 (1984), 121--126.

[37]

Conal Condren. 2014. Satire. In Encyclopedia of Humor Studies, Salvatore Attardo (Ed.). SAGE Publications.

[38]

Marlena A. Creusere. 1999. Theories of adults’ understanding and use of irony and sarcasm: Applications to and evidence from research with children. Dev. Rev. 19, 2 (1999), 213--262.

[39]

Dipto Das and Anthony J. Clark. 2018. Sarcasm detection on Facebook: A supervised learning approach. In Proceedings of the International Conference on Multimodal Interaction: Adjunct (ICMI’18). ACM, 3.

[40]

Dipto Das and Anthony J. Clark. 2018. Sarcasm detection on Flickr using a CNN. In Proceedings of the International Conference on Computing and Big Data (ICCBD’18). ACM, 56--61.

[41]

Hal Daumé, John Langford, and Daniel Marcu. 2009. Search-based structured prediction. Mach. Learn. 75, 3 (2009), 297--325.

Digital Library

[42]

Dmitry Davidov, Oren Tsur, and Ari Rappoport. 2010. Semi-supervised recognition of sarcastic sentences in Twitter and Amazon. In Proceedings of the 14th Conference on Computational Natural Language Learning (CONLL’10). ACL, 107--116.

Digital Library

[43]

Larissa A. de Freitas, Aline A. Vanin, Denise N. Hogetop, Marco N. Bochernitsan, and Renata Vieira. 2014. Pathways for irony detection in tweets. In Proceedings of the 29th Association for Computing Machinery Symposium on Applied Computing (SAC’14). ACM, 628--633.

Digital Library

[44]

Henk G. G. M. Driessen and James D. Wright. 2015. In Anthropology of Humor, James D. Wright (Ed.). Elsevier, 416--419.

[45]

Abhijeet Dubey, Aditya Joshi, and Pushpak Bhattacharyya. 2019. Deep models for converting sarcastic utterances into their non sarcastic interpretation. In Proceedings of the ACM India Joint International Conference on Data Science and Management of Data (CoDS-COMAD’19). ACM, 289--292.

Digital Library

[46]

Jonathan Dunn. 2014. Measuring metaphoricity. In Proceedings of the 52nd Meeting of the Association for Computational Linguistics (ACL’14). ACL, 745--751.

[47]

Jonathan Dunn, Jon Beltran de Heredia, Maura Burke, Lisa Gandy, Sergey Kanareykin, Oren Kapah, Matthew Taylor, Dell Hines, Ophir Frieder, David Grossman, Newton Howard, Moshe Koppel, Scott Morris, Andrew Ortony, and Shlomo Argamon. 2014. Language-independent ensemble approaches to metaphor identification. In Proceedings of the 28th Association for the Advancement of Artificial Intelligence Workshop on Cognitive Computing for Augmented Human Intelligence (AAAI’14). AAAI.

[48]

Sayandip Dutta and Anit Chakraborty. 2019. A deep learning--inspired method for social media satire detection. In Soft Computing and Signal Processing, Jiacun Wang, G. Ram Mohana Reddy, V. Kamakshi Prasad, and V. Sivakumar Reddy (Eds.). Springer, 243--251.

[49]

Anton Ermilov, Natasha Murashkina, Valeria Goryacheva, and Pavel Braslavski. 2018. Stierlitz meets SVM: Humor detection in Russian. In Proceedings of the 7th Conference on Artificial Intelligence and Natural Language (AINL’18). Springer, 178--184.

[50]

D. I. Hernández Farias and Paolo Rosso. 2017. Irony, sarcasm, and sentiment analysis. In Sentiment Analysis in Social Networks, Federico A. Pozzi, Elisabetta Fersini, Enza Messina, and Bing Liu (Eds.). Elsevier, 113--128.

[51]

Delia Irazú Hernández Farías, Viviana Patti, and Paolo Rosso. 2016. Irony detection in Twitter: The role of affective content. ACM Trans. Internet Technol. 16, 3 (June 2016), 19.

[52]

Delia Irazú Hernández Farías, Fernando Sánchez-Vega, Manuel Montes y Gómez, and Paolo Rosso. 2018. INAOE-UPV at SemEval-2018 task 3: An ensemble approach for irony detection in Twitter. In Proceedings of the 12th International Workshop on Semantic Evaluation (SemEval’18). ACL, 594--599.

[53]

Delia Irazú Hernández Farías, Emilio Sulis, Viviana Patti, Giancarlo Ruffo, and Cristina Bosco. 2015. ValenTo: Sentiment analysis of figurative language tweets with irony and sarcasm. In Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval’15). ACL, 694--698.

[54]

C. Fellbaum. 1998. WordNet. The Encyclopedia of Applied Linguistics. Wiley Online Library.

[55]

Song Feng, Jun Seok Kang, Polina Kuznetsova, and Yejin Choi. 2013. Connotation lexicon: A dash of sentiment beneath the surface meaning. In Proceedings of the 51st Meeting of the Association for Computational Linguistics (ACL’13) (Volume 1: Long Papers). ACL, 1774--1784.

[56]

Elisabetta Fersini, Federico A. Pozzi, and Enza Messina. 2015. Detecting irony and sarcasm in microblogs: The role of expressive signals and ensemble classifiers. In Proceedings of the IEEE International Conference on Data Science and Advanced Analytics (DSAA’15). IEEE, 1--8.

[57]

Elena Filatova. 2012. Irony and sarcasm: Corpus generation and analysis using crowdsourcing. In Proceedings of the 8th International Conference on Language Resources and Evaluation (LREC’12). European Language Resources Association (ELRA), 392--398.

[58]

David Fishelov. 2007. Shall I compare thee? Simile understanding and semantic categories. J. Lit. Sem. 36, 1 (Apr. 2007), 71--87.

[59]

Rudolph Flesch. 1948. A new readability yardstick. J. Applied Psych. 32, 3 (1948), 221.

[60]

Northrop Frye. 1944. The nature of satire. Univ. Toronto Quart.erly 14, 1 (1944), 75--89.

[61]

Aniruddha Ghosh, Guofu Li, Tony Veale, Paolo Rosso, Ekaterina Shutova, John Barnden, and Antonio Reyes. 2015. Semeval-2015 task 11: Sentiment analysis of figurative language in Twitter. In Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval’15). ACL, 470--478.

[62]

Aniruddha Ghosh and Tony Veale. 2016. Fracking sarcasm using neural network. In Proceedings of the 15th North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT’16). ACL, 161--169.

[63]

Raymond W. Gibbs, Gregory A. Bryant, and Herbert L. Colston. 2014. Where is the humor in verbal irony? Humor 27, 4 (2014), 575--595.

[64]

Rachel Giora. 1995. On irony and negation. Disc. Proc. 19, 2 (1995), 239--264.

[65]

Paul H. Grice. 1975. Logic and Conversation. Harward University Press. 41--58.

[66]

Charles R. Gruner. 1997. The Game of Humor: A Comprehensive Theory of Why We Laugh. Transaction Publishers.

[67]

Raj Kumar Gupta and Yinping Yang. 2017. CrystalNest at SemEval-2017 task 4: Using sarcasm detection for enhancing sentiment classification and quantification. In Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval’17). ACL, 626--633.

[68]

Patrick Hanks. 2005. Similes and sets: The English preposition like. Languages and Linguistics: Festschrift for Fr. Cermak. Charles University, Prague.

[69]

Yanfen Hao and Tony Veale. 2010. An ironic fist in a velvet glove: Creative mis-representation in the construction of ironic similes. Minds Mach. 20, 4 (2010), 635--650.

Digital Library

[70]

Jennifer Hay. 1995. Gender and Humour: Beyond a Joke. Master’s thesis. Victoria University of Wellington, Wellington, New Zealand.

[71]

Cynthia V. Hee, Els Lefever, and Véronique Hoste. 2015. LT3: Sentiment analysis of figurative tweets: Piece of cake# notreally. In Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval’15). ACL, 684--688.

[72]

Cynthia V. Hee, Els Lefever, and Véronique Hoste. 2016. Exploring the realization of irony in Twitter data. In Proceedings of the 10th International Conference on Language Resources and Evaluation (LREC’16). European Language Resources Association (ELRA), 1794--1799.

[73]

Cynthia Van Hee, Els Lefever, and Véronique Hoste. 2018. Exploring the fine-grained analysis and automatic detection of irony on Twitter. Lang. Res. Eval. 52, 3 (2018), 707--731.

Digital Library

[74]

Cynthia V. Hee, Els Lefever, and Véronique Hoste. 2018. Semeval-2018 task 3: Irony detection in English tweets. In Proceedings of the 12th International Workshop on Semantic Evaluation (SemEval’18). ACL, 39--50.

[75]

Andrew D. Hepburn. 1875. Manual of English Rhetoric. Wilson, Hinkle 8 Company.

[76]

Delia I. Hernández-Farías, Viviana Patti, and Paolo Rosso. 2018. ValenTO at SemEval-2018 task 3: Exploring the role of affective content for detecting irony in English tweets. In Proceedings of the 12th International Workshop on Semantic Evaluation (SemEval’18). ACL, 643--648.

[77]

Joyce Oramel Hertzler. 1970. Laughter: A Socio-scientific Analysis. Exposition Press.

[78]

Gilbert Highet. 1972. The Anatomy of Satire. Princeton University Press.

[79]

Yu-Hsiang Huang, Hen-Hsen Huang, and Hsin-Hsi Chen. 2017. Irony detection with attentive recurrent neural networks. In Advances in Information Retrieval, Joemon M. Jose, Claudia Hauff, Ismail Sengor Altingovde, Dawei Song, Dyaa Albakour, Stuart Watt, and John Tait (Eds.). Springer, 534--540.

[80]

Michael Israel, Jennifer Riddle Harding, and Vera Tobin. 2004. On simile. Lang. Cult. Mind 100 (2004).

[81]

Hyeju Jang, Yohan Jo, Qinlan Shen, Michael Miller, Seungwhan Moon, and Carolyn Rose. 2016. Metaphor detection with topic transition, emotion, and cognition in context. In Proceedings of the 54th Meeting of the Association for Computational Linguistics (ACL’16) (Volume 1: Long Papers). ACL, 216--225.

[82]

Hyeju Jang, Keith Maki, Eduard Hovy, and Carolyn Penstein Rose. 2017. Finding structure in figurative language: Metaphor detection with topic-based frames. In Proceedings of the 18th Conference on Special Interest Group on Discourse and Dialogue (SIGDIAL’17). ACL, 320--330.

[83]

Hyeju Jang, Seungwhan Moon, Yohan Jo, and Carolyn Rose. 2015. Metaphor detection in discourse. In Proceedings of the 16th Meeting of the Special Interest Group on Discourse and Dialogue (SIGDIAL’15). ACL, 384--392.

[84]

Yohan Jo and Alice H. Oh. 2011. Aspect and sentiment unification model for online review analysis. In Proceedings of the 4th ACM International Conference on Web Search and Data Mining (WSDM’11). ACM, 815--824.

[85]

Aditya Joshi, Pushpak Bhattacharyya, and Mark J. Carman. 2017. Automatic sarcasm detection: A survey. ACM Comput. Surv. 50, 5 (2017), 73.

Digital Library

[86]

Aditya Joshi, Diptesh Kanojia, Pushpak Bhattacharyya, and Mark James Carman. 2017. Sarcasm suite: A browser-based engine for sarcasm detection and generation. In Proceedings of the 31st Association for the Advancement of Artificial Intelligence Conference on Artificial Intelligence (AAAI’17). AAAI, 5095--5096.

[87]

Aditya Joshi, Vinita Sharma, and Pushpak Bhattacharyya. 2015. Harnessing context incongruity for sarcasm detection. In Proceedings of the 53rd Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (ACL-IJCNLP’15). ACL, 757--762.

[88]

Aditya Joshi, Vaibhav Tripathi, Pushpak Bhattacharyya, and Mark Carman. 2016. Harnessing sequence labeling for sarcasm detection in dialogue from tv series “Friends.” In Proceedings of the 20th Special Interest Group on Natural Language Learning (SIGNLL’16) Conference on Computational Natural Language Learning (CoNLL’16). ACL, 146--155.

[89]

Aditya Joshi, Vaibhav Tripathi, Pushpak Bhattacharyya, Mark James Carman, Meghna Singh, Jaya Saraswati, and Rajita Shukla. 2016. How challenging is sarcasm versus irony classification?: An analysis from human and computational perspectives. In Proceedings of the Australasian Language Technology Association Workshop (ALTA’16). Australasian Language Technology Association (ALTA), 123--127.

[90]

Aditya Joshi, Vaibhav Tripathi, Kevin Patel, Pushpak Bhattacharyya, and Mark Carman. 2016. Are word embedding-based features useful for sarcasm detection? In Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP’16). ACL, 1006--1011.

[91]

Frank Stringfellow Jr.1994. The Meaning of Irony. State University of New York.

[92]

Raquel Justo, Thomas Corcoran, Stephanie M. Lukin, Marilyn Walker, and M. Inés Torres. 2014. Extracting relevant knowledge for the detection of sarcasm and nastiness in the social web. Knowl.-based Syst. 69 (June 2014), 124--133.

[93]

Ashraf Kamal and Muhammad Abulaish. 2019. An LSTM-based deep learning approach for detecting self-deprecating sarcasm in textual data. In Proceedings of the 16th International Conference on Natural Language Processing (ICON’19). ACL, 1--10.

[94]

Ashraf Kamal and Muhammad Abulaish. 2019. Self-deprecating humor detection: A machine learning approach. In Proceedings of the 16th International Conference of the Pacific Association for Computational Linguistics (PACLING’19). Springer, 1--13.

[95]

Maria Karanasou, Christos Doulkeridis, and Maria Halkidi. 2015. DsUniPi: An SVM-based approach for sentiment analysis of figurative language on Twitter. In Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval’15). ACL, 709--713.

[96]

Jihen Karoui, Benamara Farah, Véronique Moriceau, Viviana Patti, Cristina Bosco, and Nathalie Aussenac-Gilles. 2017. Exploring the impact of pragmatic phenomena on irony detection in tweets: A multilingual corpus study. In Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics (EACL’17), Vol. 1. ACL, 262--272.

[97]

Jihen Karoui, Farah B. Zitoune, Véronique Moriceaue, Nathalie Aussenac-Gilles, and Lamia H. Belguith. 2015. Towards a contextual pragmatic model to detect irony in tweets. In Proceedings of the 53rd Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (ACL-IJCNLP’15). ACL, 644--650.

[98]

Ankush Khandelwal, Sahil Swami, Syed S. Akhtar, and Manish Shrivastava. 2018. Humor Detection in English-Hindi Code-mixed Social Media Content: Corpus and Baseline System. arXiv preprint arXiv:1806.05513.

[99]

Anupam Khattri, Aditya Joshi, Pushpak Bhattacharyya, and Mark J. Carman. 2015. Your sentiment precedes you: Using an author’s historical tweets to predict sarcasm. In Proceedings of the 6th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis (WASSA-ACL’15). ACL, 25--30.

[100]

Maria Khokhlova, Viviana Patti, and Paolo Rosso. 2016. Distinguishing between irony and sarcasm in social media texts: Linguistic observations. In Proceedings of the International FRUCT Conference on Intelligence, Social Media and Web (ISMW FRUCT’16). IEEE, 1--6.

[101]

Charles A. Knight. 2004. The Literature of Satire. Cambridge University Press.

[102]

Efstratios Kontopoulos, Christos Berberidis, Theologos Dergiades, and Nick Bassiliades. 2013. Ontology-based sentiment analysis of Twitter posts. Expert Syst. Appl. 40, 10 (2013), 4065--4074.

Digital Library

[103]

Roger J. Kreuz and Gina M. Caucci. 2007. Lexical influences on the perception of sarcasm. In Proceedings of the Workshop on Computational Approaches to Figurative Language (ACL’07). ACL, 1--4.

[104]

Roger J. Kreuz and Richard M. Roberts. 1995. Two cues for verbal irony: Hyperbole and the ironic tone of voice. Metaph. Symb. 10, 1 (1995), 21--31.

[105]

Sachi Kumon-Nakamura, Sam Glucksberg, and Mary Brown. 1995. How about another piece of pie: The allusional pretense theory of discourse irony. J. Exper. Psych.: Gen. 124, 1 (Mar. 1995), 3.

[106]

Florian Kunneman, Christine Liebrecht, Margot V. Mulken, and Antal V. D. Bosch. 2015. Signaling sarcasm: From hyperbole to hashtag. Inform. Proc. Manag. 51, 4 (Aug. 2015), 500--509.

Digital Library

[107]

Heather L. LaMarre, Kristen D. Landreville, and Michael A. Beam. 2009. The irony of satire: Political ideology and the motivation to see what you want to see in the Colbert Report. Int. J. Press/Polit. 14, 2 (2009), 212--231.

[108]

G. Harry McLaughlin. 1969. SMOG grading—A new readability formula. J. Read. 12, 8 (1969), 639--646.

[109]

Yann LeCun, Yoshua Bengio, and Geoffrey Hinton. 2015. Deep learning. Nature 521, 7553 (2015), 436--444.

[110]

John S. Leggitt and Raymond W. Gibbs. 2000. Emotional reactions to verbal irony. Disc. Proc. 29, 1 (2000), 1--24.

[111]

Christine Liebrecht, Florian Kunneman, and Antal V. D. Bosch. 2013. The perfect solution for detecting sarcasm in tweets #not. In Proceedings of the 4th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis (WASSA-ACL’13). ACL, 29--37.

[112]

Jennifer Ling and Roman Klinger. 2016. An empirical, quantitative analysis of the differences between sarcasm and irony. In Proceedings of the International Semantic Web Conference (ESWC’16), Harald Sack, Giuseppe Rizzo, Nadine Steinmetz, Dunja Mladenić, Sren Auer, and Christoph Lange (Eds.). Springer, 203--216.

[113]

Nick Littlestone. 1988. Learning quickly when irrelevant attributes abound: A new linear-threshold algorithm. Mach. Learn. 2, 4 (1988), 285--318.

[114]

Lizhen Liu, Xiao Hu, Wei Song, Ruiji Fu, Ting Liu, and Guoping Hu. 2018. Neural multitask learning for simile recognition. In Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP’18). ACL, 1543--1553.

[115]

Lizhen Liu, Donghai Zhang, and Wei Song. 2018. Exploiting syntactic structures for humor recognition. In Proceedings of the 27th International Conference on Computational Linguistics (COLING’18). 1875--1883.

[116]

Lizhen Liu, Donghai Zhang, and Wei Song. 2018. Modeling sentiment association in discourse for humor recognition. In Proceedings of the 56th Meeting of the Association for Computational Linguistics (ACL’18) (Short Papers). ACL, 586--591.

[117]

Peng Liu, Wei Chen, Gaoyan Ou, Tengjiao Wang, Dongqing Yang, and Kai Lei. 2014. Sarcasm detection in social media based on imbalanced classification. In Web-Age Information Management, Feifei Li, Guoliang Li, Seung won Hwang, Bin Yao, and Zhenjie Zhang (Eds.). Springer International Publishing, 459--471.

[118]

Joan Lucariello. 2007. Situational irony: A concept of events gone away. Irony in Language and Thought: A Cognitive Science Reader. Psychology Press, 467--498.

[119]

Stephanie Lukin and Marilyn Walker. 2013. Really? Well. Apparently bootstrapping improves the performance of sarcasm and nastiness classifiers for online dialogue. In Proceedings of the Workshop on Language in Social Media (ACL’13). ACL, 30--40.

[120]

Edwin Lunando and Ayu Purwarianti. 2013. Indonesian social media sentiment analysis with sarcasm detection. In Proceedings of the International Conference on Advanced Computer Science and Information Systems (ICACSIS’13). IEEE, 195--198.

[121]

P. D. Manjusha and C. Raseek. 2018. Convolutional neural network based simile classification system. In Proceedings of the IEEE International Conference on Emerging Trends and Innovations in Engineering and Technological Research (ICETIETR’18). IEEE, 1--5.

[122]

Jill Mann. 1973. Chaucer and Medieval Estates Satire: The Literature of Social Classes and the General Prologue to the Canterbury Tales. The University of Chicago Press.

[123]

James H. Martin. 1990. A Computational Model of Metaphor Interpretation. Academic Press Professional, Inc.

[124]

Zachary J. Mason. 2004. CorMet: A computational, corpus-based conventional metaphor extraction system. Comput. Ling. 30, 1 (2004), 23--44.

Digital Library

[125]

Diana Maynard and Mark A. Greenwood. 2014. Who cares about sarcastic tweets? Investigating the impact of sarcasm on sentiment analysis. In Proceedings of the 9th International Conference on Language Resources and Evaluation (LREC’14). European Language Resources Association (ELRA), 4238--4243.

[126]

Skye McDonald. 2007. Neuropsychological studies of sarcasm. In Irony in Language and Thought, Raymond W. Gibbs Jr. and Herbert L. Colston (Eds.). Psychology Press, 217--230.

[127]

Sarah McGillion, Héctor M. Alonso, and Barbara Plank. 2015. CPH: Sentiment analysis of figurative language on Twitter #easypeasy #not. In Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval’15). ACL, 699--703.

[128]

Rada Mihalcea and Stephen Pulman. 2007. Characterizing humour: An exploration of features in humorous texts. In Proceedings of the 16th Conference on the International Conference on Intelligent Text Processing and Computational Linguistics (CICLing’07). Springer, 337--347.

Digital Library

[129]

Rada Mihalcea and Carlo Strapparava. 2005. Computational laughing: Automatic recognition of humorous one-liners. In Proceedings of the Cognitive Science Conference. Citeseer, 1513--1518.

[130]

Rada Mihalcea and Carlo Strapparava. 2005. Making computers laugh: Investigations in automatic humor recognition. In Proceedings of the Conference on Human Language Technology and Empirical Methods in Natural Language Processing (HLT-EMNLP’05). ACL, 531--538.

Digital Library

[131]

Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S. Corrado, and Jeff Dean. 2013. Distributed representations of words and phrases and their compositionality. In Proceedings of the Conference on Advances in Neural Information Processing Systems (NIPS’13). 3111--3119.

Digital Library

[132]

George A. Miller. 1995. WordNet: A lexical database for English. Commun. ACM 38, 11 (1995), 39--41.

Digital Library

[133]

Abhijit Mishra, Diptesh Kanojia, and Pushpak Bhattacharyya. 2016. Predicting readers’ sarcasm understandability by modeling gaze behavior. In Proceedings of the 13th Association for the Advancement of Artificial Intelligence Conference on Artificial Intelligence (AAAI’16). AAAI.

[134]

Abhijit Mishra, Diptesh Kanojia, Abhijit Nagar, Kuntal Dey, and Pushpak Bhattacharyya. 2016. Harnessing cognitive features for sarcasm detection. In Proceedings of the 54th Meeting of the Association for Computational Linguistics (ACL’16). ACL, 1095--1104.

[135]

Saif Mohammad, Cody Dunne, and Bonnie Dorr. 2009. Generating high-coverage semantic orientation lexicons from overtly marked words and a thesaurus. In Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP’09). ACL, 599--608.

[136]

Michael Mohler, David Bracewell, David Hinote, and Marc Tomlinson. 2013. Semantic signatures for example-based linguistic metaphor detection. In Proceedings of the 1st Workshop on Metaphor in Natural Language Processing (Meta4NLP’13). ACL, 27--35.

[137]

Michael Mohler, Marc Tomlinson, and Bryan Rink. 2015. Cross-lingual semantic generalization for the detection of metaphor. Int. J. Comput. Ling. Appl. 6, 2 (Feb. 2015), 117--140.

[138]

Alex Morales and ChengXiang Zhai. 2017. Identifying humor in reviews using background text sources. In Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP’17). ACL, 492--501.

[139]

Anna Mosolova, Ivan Bondarenko, and Vadim Fomin. 2018. Conditional random fields for metaphor detection. In Proceedings of the Workshop on Figurative Language Processing. ACL, 121--123.

[140]

Suzanne Mpouli. 2017. Annotating similes in literary texts. In Proceedings of the 13th Joint ISO-ACL Workshop on Interoperable Semantic Annotation (ISA’17). 26--36.

[141]

Douglas C. Muecke. 1982. Irony and the Ironic. Methuen.

[142]

Shubhadeep Mukherjee and Pradip Kumar Bala. 2017. Sarcasm detection in microblogs using naïve Bayes and fuzzy clustering. Tech. Soc. 48 (2017), 19--27.

[143]

Smaranda Muresan, Roberto Gonzalez-Ibanez, Debanjan Ghosh, and Nina Wacholder. 2016. Identification of nonliteral language in social media: A case study on sarcasm. J. Assoc. Inform. Sci. Technol. 67, 11 (Nov. 2016), 2725--2737.

[144]

Constantine Nakassis and Jesse Snedeker. 2001. Beyond sarcasm: Intonation and context as relational cues in children’s recognition of irony. In Proceedings of the 26th Boston University Conference on Language Development (BUCLD’01). Cascadilla Press, Somerville, MA, 429--440.

[145]

Roberto González-Ibánez, Smaranda Muresan, and Nina Wacholder. 2011. Identifying sarcasm in Twitter: A closer look. In Proceedings of the 49th Meeting of the Association for Computational Linguistics (ACL’11). ACL, 581--586.

[146]

Hoang L. Nguyen and Jai E. Jung. 2016. Statistical approach for figurative sentiment analysis on social networking services: A case study on Twitter. Multim. Tools Appl. (Apr. 2016), 1--14.

[147]

Hoang L. Nguyen, Trung D. Nguyen, and Dosam Hwang. 2015. Kelabteam: A statistical approach on figurative language sentiment analysis in Twitter. In Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval’15). ACL, 679--683.

[148]

Vlad Niculae and Cristian Danescu-Niculescu-Mizil. 2014. Brighter than gold: Figurative language in user generated comparisons. In Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP’14). ACL, 2008--2018.

[149]

Finn Årup Nielsen. 2011. A new ANEW: Evaluation of a word list for sentiment analysis in microblogs. In Proceedings of the Workshop on Making Sense of Microposts: Big Things Come in Small Packages (ESWC’11). 93--98.

[150]

Luke De Oliveira and Alfredo L. Rodrigo. 2015. Humor detection in Yelp reviews. Retrieved on December 15, 2019 from https://cs224d.stanford.edu/reports/OliveiraLuke.pdf.

[151]

Silviu Oprea and Walid Magdy. 2019. iSarcasm: A Dataset of Intended Sarcasm. arXiv preprint arXiv:1911.03123.

[152]

Reynier Ortega-Bueno, Carlos E. Muniz-Cuza, José E. Medina Pagola, and Paolo Rosso. 2018. UO UPV: Deep linguistic humor detection in Spanish social media. In Proceedings of the 3rd Workshop on Evaluation of Human Language Technologies for Iberian Languages (IberEval’18) co-located with the 34th Conference of the Spanish Society for Natural Language Processing (SEPLN’18). 204--213.

[153]

Reynier Ortega-Bueno, Francisco Rangel, Delia I. H. Farıas, Paolo Rosso, Manuel Montes y Gómez, and José E. Medina-Pagola. 2019. Overview of the task on irony detection in Spanish variants. In Proceedings of the Iberian Languages Evaluation Forum (IberLEF’19), co-located with the 35th Conference of the Spanish Society for Natural Language Processing (SEPLN’19). CEUR-WS.org, 229--256.

[154]

Reynier Ortega-Bueno, Paolo Rosso, and José E. Medina Pagola. 2019. UO UPV2 at HAHA 2019: BiGRU neural network informed with linguistic features for humor recognition. In Proceedings of the Iberian Languages Evaluation Forum (IberLEF’19), co-located with the 35th Conference of the Spanish Society for Natural Language Processing (SEPLN’19). CEUR-WS.org, 212--221.

[155]

Endang W. Pamungkas and Viviana Patti. 2018. #NonDicevoSulSerio at SemEval-2018 task 3: Exploiting emojis and affective content for irony detection in English tweets. In Proceedings of the 12th International Workshop on Semantic Evaluation (SemEval’18). ACL, 649--654.

[156]

Natalie Parde and Rodney Nielsen. 2018. Detecting sarcasm is extremely easy;-. In Proceedings of the Workshop on Computational Semantics beyond Events and Roles (SemBEaR’18). ACL, 21--26.

[157]

Krishna Parmar, Nivid Limbasiya, and Maulik Dhamecha. 2018. Feature based composite approach for sarcasm detection using MapReduce. In Proceedings of the 2nd International Conference on Computing Methodologies and Communication (ICCMC’18). IEEE, 587--591.

[158]

Ted Pedersen, Siddharth Patwardhan, and Jason Michelizzi. 2004. WordNet:: Similarity: Measuring the relatedness of concepts. In Demonstration Papers at Human Language Technologies: North American Chapter of the Association for Computational Linguistics (HLT-NAACL’04). ACL, 38--41.

[159]

Bo Peng, Jin Wang, and Xuejie Zhang. 2018. YNU-HPCC at SemEval-2018 task 3: Ensemble neural network models for irony detection on Twitter. In Proceedings of the 12th International Workshop on Semantic Evaluation (SemEval’18). ACL, 622--627.

[160]

John Peter. 1956. Complaint and Satire in Early English Literature. Clarendon.

[161]

Soujanya Poria, Erik Cambria, Devamanyu Hazarika, and Prateek Vij. 2016. A deeper look into sarcastic tweets using deep convolutional neural networks. In Proceedings of the 25th International Conference on Computational Linguistics (COLING’16). 1601--1612.

[162]

Peter Potash, Alexey Romanov, and Anna Rumshisky. 2017. SemEval-2017 task 6:# Hashtagwars: Learning a sense of humor. In Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval’17). ACL, 49--57.

[163]

Malay Pramanick, Ashim Gupta, and Pabitra Mitra. 2018. An LSTM-CRF based approach to token-level metaphor detection. In Proceedings of the Workshop on Figurative Language Processing. ACL, 67--75.

[164]

Malay Pramanick and Pabitra Mitra. 2018. Unsupervised detection of metaphorical adjective-noun pairs. In Proceedings of the Workshop on Figurative Language Processing. ACL, 76--80.

[165]

Tomás Ptácek, Ivan Habernal, and Jun Hong. 2014. Sarcasm detection on Czech and English Twitter. In Proceedings of the 25th International Conference on Computational Linguistics (COLING’14). 213--223.

[166]

Ashequl Qadir, Ellen Riloff, and Marilyn A. Walker. 2015. Learning to recognize affective polarity in similes. In Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP’15). ACL, 190--200.

[167]

Ashequl Qadir, Ellen Riloff, and Marilyn A. Walker. 2016. Automatically inferring implicit properties in similes. In Proceedings of the 15th Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT’16). ACL, 1223--1232.

[168]

Ashwin Rajadesingan, Reza Zafarani, and Huan Liu. 2015. Sarcasm detection on Twitter: A behavioral modeling approach. In Proceedings of the 8th Association for Computing Machinery International Conference on Web Search and Data Mining (WSDM’15). ACM, 97--106.

Digital Library

[169]

Victor Raskin. 1979. Semantic mechanisms of humor. In Proceedings of the 5th Meeting of the Berkeley Linguistics Society. 325--335.

[170]

Victor Raskin, Christian F. Hempelmann, and Julia M. Taylor. 2009. How to understand and assess a theory: The evolution of the SSTH into the GTVH and now into the OSTH. J. Lit. Theor. 3, 2 (2009), 285--311.

[171]

Kumar Ravi and Vadlamani Ravi. 2017. A novel automatic satire and irony detection using ensembled feature selection and data mining. Knowl.-based Syst. 120 (2017), 15--33.

[172]

Kumar Ravi and Vadlamani Ravi. 2018. Irony detection using neural network language model, psycholinguistic features, and text mining. In Proceedings of the 12th IEEE International Conference on Cognitive Informatics 8 Cognitive Computing (ICCI*CC’18). IEEE, 254--260.

[173]

Raymond W. Gibbs Jr. and Herbert L. Colston. 2007. Irony in Language and Thought: A Cognitive Science Reader. Psychology Press.

[174]

Yishay Raz. 2012. Automatic humor classification on Twitter. In Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Student Research Workshop (NAACL HLT’12). ACL, 66--70.

[175]

Aishwarya N. Reganti, Tushar Maheshwari, Upendra Kumar, Amitava Das, and Rajiv Bajpai. 2016. Modeling satire in English text for automatic detection. In Proceedings of the Workshop on Sentiment Elicitation from Natural Text for Information Retrieval and Extraction (SENTIRE’16). IEEE.

[176]

Antonio Reyes and Paolo Rosso. 2011. Mining subjective knowledge from customer reviews: A specific case of irony detection. In Proceedings of the 2nd Workshop on Computational Approaches to Subjectivity and Sentiment Analysis (ACL-HLT’11). ACL, 118--124.

[177]

Antonio Reyes and Paolo Rosso. 2012. Making objective decisions from subjective data: Detecting irony in customer reviews. Dec. Supp. Syst. 53, 4 (May 2012), 754--760.

[178]

Antonio Reyes and Paolo Rosso. 2014. On the difficulty of automatically detecting irony: Beyond a simple case of negation. Knowl. Inform. Syst. 40, 3 (2014), 595--614.

Digital Library

[179]

Antonio Reyes, Paolo Rosso, and Davide Buscaldi. 2012. From humor recognition to irony detection: The figurative language of social media. Data Knowl. Eng. 74 (June 2012), 1--12.

[180]

Antonio Reyes, Paolo Rosso, and Tony Veale. 2013. A multidimensional approach for detecting irony in Twitter. Lang. Resour. Eval. 47, 1 (2013), 239--268.

Digital Library

[181]

Ellen Riloff, Ashequl Qadir, Prafulla Surve, Lalindra D. Silva, Nathan Gilbert, and Ruihong Huang. 2013. Sarcasm as contrast between a positive sentiment and negative situation. In Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP’13). ACL, 704--714.

[182]

Charlotte Roze, Laurence Danlos, and Philippe Muller. 2012. LEXCONN: A French lexicon of discourse connectives. Disc., Multidisc. Persp. Sig. Text Org. 10 (2012).

[183]

Victoria L. Rubin, Niall J. Conroy, Yimin Chen, and Sarah Cornwell. 2016. Fake news or truth? Using satirical cues to detect potentially misleading news. In Proceedings of the 15th Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT’16). ACL, 7--17.

[184]

Willibald Ruch. 2001. The perception of humor. In Emotions, Qualia, and Consciousness, Alfred Kaszniak (Ed.). World Scientific, 410--425.

[185]

Jason Rutter. 1997. Stand-Up as Interaction: Performance and Audience in Comedy Venues. Ph.D. Thesis. University of Salford, UK.

[186]

María Del Pilar Salas-Zárate, Mario A. Paredes-Valverde, Miguel A. Rodriguez-García, Rafael Valencia-García, and Giner Alor-Hernández. 2017. Automatic detection of satire in Twitter: A psycholinguistic-based approach. Knowl.-based Syst. 128 (2017), 20--33.

[187]

Mary Jane C. Samonte, Carl Justine T. Dollete, Paolo Mikkael M. Capanas, Maristela Louise C. Flores, and Caroline B. Soriano. 2018. Sentence-level sarcasm detection in English and Filipino tweets. In Proceedings of the 4th International Conference on Industrial and Business Engineering (ICIBE’18). ACM, 181--186.

[188]

Sushmitha Reddy Sane, Suraj Tripathi, Koushik Reddy Sane, and Radhika Mamidi. 2019. Deep learning techniques for humor detection in Hindi-English code-mixed tweets. In Proceedings of the 10th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis. ACL, 57--61.

[189]

Sohan De Sarkar, Fan Yang, and Arjun Mukherjee. 2018. Attending sentences to detect satirical fake news. In Proceedings of the 27th International Conference on Computational Linguistics (COLING’18). 3371--3380.

[190]

Leon Satterfield. 1982. The ironic sign. In Semiotics 1980, Michael Herzfeld and Margot D. Lenbart (Eds.). Springer Science 8 Business Media, 467--474.

[191]

Rossano Schifanella, Paloma D. Juan, Joel Tetreault, and Liangliang Cao. 2016. Detecting sarcasm in multimodal social platforms. In Proceedings of the ACM on Multimedia Conference (ACM MM’16). ACM, 1136--1145.

Digital Library

[192]

R. J. Senter and Edgar A. Smith. 1967. Automated Readability Index. Technical Report. University of Cincinnati, Ohio, USA.

[193]

Dafna Shahaf, Eric Horvitz, and Robert Mankoff. 2015. Inside jokes: Identifying humorous cartoon captions. In Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD’15). ACM, 1065--1074.

Digital Library

[194]

Ekaterina Shutova, Lin Sun, and Anna Korhonen. 2010. Metaphor identification using verb and noun clustering. In Proceedings of the 23rd International Conference on Computational Linguistics (COLING’10). 1002--1010.

Digital Library

[195]

Dan Sperber and Deirdre Wilson. 1981. Irony and the use-mention distinction. In Radical Pragmatics, Peter Cole (Ed.). Elsevier, 295--318.

[196]

Andreas Stöckl. 2018. Detecting Satire in the News with Machine Learning. arXiv preprint arXiv:1810.00593.

[197]

Marco Stranisci, Cristina Bosco, Delia I. H. Farìas, and Viviana Patti. 2016. Annotating sentiment and irony in the online Italian political debate on #labuonascuola. In Proceedings of the 10th International Conference on Language Resources and Evaluation (LREC’16). European Language Resources Association (ELRA), 2892--2899.

[198]

Emilio Sulis, Delia I. H. Farías, Paolo Rosso, Viviana Patti, and Giancarlo Ruffo. 2016. Figurative messages and affect in Twitter: Differences between #irony, #sarcasm, and #not. Knowl.-based Syst. 108 (May 2016), 132--143.

[199]

Krishnkant Swarnkar and Anil Kumar Singh. 2018. Di-LSTM contrast: A deep neural network for metaphor detection. In Proceedings of the Workshop on Figurative Language Processing. ACL, 115--120.

[200]

Yi-jie Tang and Hsin-Hsi Chen. 2014. Chinese irony corpus construction and ironic structure analysis. In Proceedings of the 25th International Conference on Computational Linguistics (COLING’14). 1269--1278.

[201]

Hande Taslioglu and Pinar Karagoz. 2017. Irony detection on microposts with limited set of features. In Proceedings of the 32nd ACM Symposium on Applied Computing (SAC’17). ACM, 1076--1081.

Digital Library

[202]

Julia M. Taylor and Lawrence J. Mazlack. 2004. Computationally recognizing wordplay in jokes. In Proceedings of the 26th Meeting of the Cognitive Science Society (CogSci’04).

[203]

Serra Sinem Tekiroglu, Gözde Özbal, and Carlo Strapparava. 2014. Sensicon: An automatically constructed sensorial lexicon. In Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP’14). 1511--1521.

[204]

Joseph Tepperman, David R. Traum, and Shrikanth Narayanan. 2006. “Yeah right”: Sarcasm recognition for spoken dialogue systems. In Proceedings of the 9th International Conference on Spoken Language Processing (ICSLP’06). International Speech Communication Association (ISCA), 1838--1841.

[205]

Pyae Phyo Thu and Nwe Nwe. 2017. Impact analysis of emotion in figurative language. In Proceedings of the 16th IEEE/ACIS International Conference on Computer and Information Science (ICIS’17). IEEE, 209--214.

[206]

Pyae Phyo Thu and Nwe Nwe. 2017. Implementation of emotional features on satire detection. In Proceedings of the 18th IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD’17). IEEE, 149--154.

[207]

Enrica Troiano, Carlo Strapparava, Gözde Özbal, and Serra Sinem Tekiroglu. 2018. A computational exploration of exaggeration. In Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP’18). ACL, 3296--3304.

[208]

Oren Tsur, Dmitry Davidov, and Ari Rappoport. 2010. ICWSM-A great catchy name: Semi-supervised recognition of sarcastic sentences in online product reviews. In Proceedings of the 4th International Association for the Advancement of Artificial Intelligence Conference on Weblogs and Social Media (ICWSM’10). AAAI, 162--169.

[209]

Yulia Tsvetkov, Leonid Boytsov, Anatole Gershman, Eric Nyberg, and Chris Dyer. 2014. Metaphor detection with cross-lingual model transfer. In Proceedings of the 52nd Meeting of the Association for Computational Linguistics (ACL’14) (Long Papers), Vol. 1. ACL, 248--258.

[210]

Piyoros Tungthamthiti, Shirai Kiyoaki, and Masnizah Mohd. 2014. Recognition of sarcasms in tweets based on concept level sentiment analysis and supervised learning approaches. In Proceedings of the 28th Pacific Asia Conference on Language, Information and Computation (PACLIC’14). 404--413.

[211]

Akira Utsumi. 1996. A unified theory of irony and its computational formalization. In Proceedings of the 16th Conference on Computational Linguistics-Volume 2 (COLING’96). 962--967.

Digital Library

[212]

Tony Veale and Yanfen Hao. 2007. Comprehending and generating apt metaphors: A web-driven, case-based approach to figurative language. In Proceedings of the 22nd Conference of the Association for the Advancement of Artificial Intelligence (AAAI’07), Vol. 2. AAAI, 1471--1476.

[213]

Thanh Vu, Dat Quoc Nguyen, Xuan-Son Vu, Dai Quoc Nguyen, Michael Catt, and Michael Trenell. 2018. NIHRIO at SemEval-2018 task 3: A Simple and Accurate Neural Network Model for Irony Detection in Twitter. arXiv preprint arXiv:1804.00520.

[214]

Byron C. Wallace. 2015. Computational irony: A survey and new perspectives. Artif. Intell. Rev. 43, 4 (2015), 467--483.

Digital Library

[215]

Byron C. Wallace, Do K. Choe, and Eugene Charniak. 2015. Sparse, contextually informed models for irony detection: Exploiting user communities, entities, and sentiment. In Proceedings of the 53rd Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (ACL-IJCNLP’15). ACL, 1035--1044.

[216]

Po-Ya Angela Wang. 2013. #Irony or #sarcasm—A quantitative and qualitative study based on Twitter. In Proceedings of the 27th Pacific Asia Conference on Language, Information and Computing (PACLIC’13). 349--356.

[217]

Zelin Wang, Zhijian Wu, Ruimin Wang, and Yafeng Ren. 2015. Twitter sarcasm detection exploiting a context-based model. In Web Information Systems Engineering (WISE), Jianyong Wang, Wojciech Cellary, Dingding Wang, Hua Wang, Shu-Ching Chen, Tao Li, and Yanchun Zhang (Eds.). Springer, Cham, 77--91.

[218]

Cynthia Whissell. 2009. Using the revised dictionary of affect in language to quantify the emotional undertones of samples of natural language. Psych. Rep. 105, 2 (2009), 509--521.

[219]

Deirdre Wilson. 2006. The pragmatics of verbal irony: Echo or pretence? Lingua 116, 10 (May 2006), 1722--1743.

[220]

Deirdre Wilson and Dan Sperber. 1992. On verbal irony. Lingua 87, 1 (1992), 53--76.

[221]

Theresa Wilson, Janyce Wiebe, and Paul Hoffmann. 2005. Recognizing contextual polarity in phrase-level sentiment analysis. In Proceedings of the Conference on Human Language Technology and Empirical Methods in Natural Language Processing (HLT-EMNLP’05). ACL, 347--354.

Digital Library

[222]

Chuhan Wu, Fangzhao Wu, Yubo Chen, Sixing Wu, Zhigang Yuan, and Yongfeng Huang. 2018. Neural metaphor detecting with CNN-LSTM model. In Proceedings of the Workshop on Figurative Language Processing. ACL, 110--114.

[223]

Hongzhi Xu, Enrico Santus, Anna Laszlo, and Chu-Ren Huang. 2015. LLT-PolyU: Identifying sentiment intensity in ironic tweets. In Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval’15). ACL, 673--678.

[224]

Diyi Yang, Alon Lavie, Chris Dyer, and Eduard Hovy. 2015. Humor recognition and humor anchor extraction. In Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP’15). ACL, 2367--2376.

[225]

Donghai Zhang, Wei Song, Lizhen Liu, Chao Du, and Xinlei Zhao. 2017. Investigations in automatic humor recognition. In Proceedings of the 10th International Symposium on Computational Intelligence and Design (ISCID’17). IEEE, 272--275.

[226]

Meishan Zhang, Yue Zhang, and Guohong Fu. 2016. Tweet sarcasm detection using deep neural network. In Proceedings of the 26th International Conference on Computational Linguistics (COLING’16). 2449--2460.

[227]

Renxian Zhang and Naishi Liu. 2014. Recognizing humor on Twitter. In Proceedings of the 23rd ACM International Conference on Information and Knowledge Management (CIKM’14). ACM, 889--898.

Digital Library

[228]

Shiwei Zhang, Xiuzhen Zhang, Jeffrey Chan, and Paolo Rosso. 2019. Irony detection via sentiment-based transfer learning. Inform. Proc. 8 Manag. 56, 5 (2019), 1633--1644.

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cover image ACM Transactions on the Web

ACM Transactions on the Web Volume 14, Issue 1

February 2020

133 pages

ISSN:1559-1131

EISSN:1559-114X

DOI:10.1145/3378674

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Lehigh University, USA

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Publication History

Published: 07 February 2020

Accepted: 01 December 2019

Revised: 01 November 2019

Received: 01 March 2019

Published in TWEB Volume 14, Issue 1

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