Exploring the Impact of Pragmatic Phenomena on Irony Detection in Tweets: A Multilingual Corpus Study

This paper provides a linguistic and pragmatic analysis of the phenomenon of irony in order to represent how Twitter's users exploit irony devices within their communication strategies for generating textual contents. We aim to measure the impact of a wide-range of pragmatic phenomena in the interpretation of irony, and to investigate how these phenomena interact with contexts local to the tweet. Informed by linguistic theories, we propose for the first time a multi-layered annotation schema for irony and its application to a corpus of French, English and Italian tweets.We detail each layer, explore their interactions, and discuss our results according to a qualitative and quantitative perspective.

[1]  Jun Hong,et al.  Sarcasm Detection on Czech and English Twitter , 2014, COLING.

[2]  Diana Maynard,et al.  Who cares about Sarcastic Tweets? Investigating the Impact of Sarcasm on Sentiment Analysis. , 2014, LREC.

[3]  Byron C. Wallace,et al.  Sparse, Contextually Informed Models for Irony Detection: Exploiting User Communities, Entities and Sentiment , 2015, ACL.

[4]  Pushpak Bhattacharyya,et al.  Harnessing Context Incongruity for Sarcasm Detection , 2015, ACL.

[5]  Ari Rappoport,et al.  Semi-Supervised Recognition of Sarcasm in Twitter and Amazon , 2010, CoNLL.

[6]  Edward Nelson,et al.  Syntax and Semantics , 1974 .

[7]  Philipp Cimiano,et al.  An Impact Analysis of Features in a Classification Approach to Irony Detection in Product Reviews , 2014, WASSA@ACL.

[8]  Philippe Niogret Les figures de l'ironie dans A la recherche du temps perdu de Marcel Proust , 2004 .

[9]  Antal van den Bosch,et al.  The perfect solution for detecting sarcasm in tweets #not , 2013, WASSA@NAACL-HLT.

[10]  Paolo Rosso,et al.  A multidimensional approach for detecting irony in Twitter , 2013, Lang. Resour. Evaluation.

[11]  Véronique Hoste,et al.  Exploring the Realization of Irony in Twitter Data , 2016, LREC.

[12]  Malvina Nissim,et al.  Sentiment analysis on Italian tweets , 2013, WASSA@NAACL-HLT.

[13]  D. Sperber,et al.  Irony and the Use-Mention Distinction , 1981 .

[14]  Katharina Barbe Irony in context , 1995 .

[15]  Ken-ichi Seto On non-echoic irony , 1998 .

[16]  John M. Kennedy,et al.  Unresolved contradictions specifying attitudes — in metaphor, irony, understatement and tautology , 1996 .

[17]  R. Gibbs Irony in Talk Among Friends , 2000 .

[18]  J. Haiman Talk Is Cheap: Sarcasm, Alienation, and the Evolution of Language , 1998 .

[19]  David B. Ritchie,et al.  Frame-Shifting in Humor and Irony , 2005 .

[20]  Paolo Rosso,et al.  SemEval-2015 Task 11: Sentiment Analysis of Figurative Language in Twitter , 2015, *SEMEVAL.

[21]  Malvina Nissim,et al.  Overview of the Evalita 2014 SENTIment POLarity Classification Task , 2014 .

[22]  Cristina Bosco,et al.  ValenTo: Sentiment Analysis of Figurative Language Tweets with Irony and Sarcasm , 2015, SemEval@NAACL-HLT.

[23]  Nathalie Aussenac-Gilles,et al.  Towards a Contextual Pragmatic Model to Detect Irony in Tweets , 2015, ACL.

[24]  Roland Hausser,et al.  Principles of Pragmatics , 1989 .

[25]  Malvina Nissim,et al.  Overview of the Evalita 2016 SENTIment POLarity Classification Task , 2014, CLiC-it/EVALITA.

[26]  Cristina Bosco,et al.  Annotating Sentiment and Irony in the Online Italian Political Debate on #labuonascuola , 2016, LREC.

[27]  RossoPaolo,et al.  A multidimensional approach for detecting irony in Twitter , 2013 .

[28]  Ari Rappoport,et al.  ICWSM - A Great Catchy Name: Semi-Supervised Recognition of Sarcastic Sentences in Online Product Reviews , 2010, ICWSM.

[29]  Paolo Rosso,et al.  Figurative messages and affect in Twitter: Differences between #irony, #sarcasm and #not , 2016, Knowl. Based Syst..

[30]  David Bamman,et al.  Contextualized Sarcasm Detection on Twitter , 2015, ICWSM.

[31]  Akira Utsumi,et al.  A Unified Theory of Irony and Its Computational Formalization , 1996, COLING.

[32]  Jacob Cohen Statistical Power Analysis for the Behavioral Sciences , 1969, The SAGE Encyclopedia of Research Design.

[33]  Christian Burgers,et al.  Verbal irony: Use and effects in written discourse , 2006 .

[34]  Nina Wacholder,et al.  Identifying Sarcasm in Twitter: A Closer Look , 2011, ACL.

[35]  Paolo Rosso,et al.  Irony Detection in Twitter , 2016, ACM Trans. Internet Techn..

[36]  Cristina Bosco,et al.  Developing Corpora for Sentiment Analysis: The Case of Irony and Senti-TUT , 2013, IEEE Intelligent Systems.

[37]  R. Gibbs The Poetics of Mind: Figurative Thought, Language, and Understanding , 1994 .

[38]  Albert N. Katz,et al.  The Differential Role of Ridicule in Sarcasm and Irony , 1998 .

[39]  Alice Myers Roy,et al.  Irony in conversation , 1989 .

[40]  Horacio Saggion,et al.  Modelling Irony in Twitter: Feature Analysis and Evaluation , 2014, LREC.

[41]  Hsin-Hsi Chen,et al.  Chinese Irony Corpus Construction and Ironic Structure Analysis , 2014, COLING.

[42]  Cristina Bosco,et al.  Tweeting and Being Ironic in the Debate about a Political Reform: the French Annotated Corpus TWitter-MariagePourTous , 2016, LREC.

[43]  Richard J. Gerrig,et al.  On the pretense theory of irony. , 1984, Journal of experimental psychology. General.

[44]  Lucie Didio Une approche sémantico-sémiotique de l'ironie , 2013 .

[45]  Timothy Baldwin,et al.  Automatic Satire Detection: Are You Having a Laugh? , 2009, ACL.

[46]  Cameron Shelley,et al.  The bicoherence theory of situational irony , 2001, Cogn. Sci..