Verbal irony and the Maxims of Grice's cooperative principle

This paper makes reference to Brown and Levinson's Politeness Theory, and their treatment of irony as an off record strategy. These authors consider that different off record strategies violate or flout different maxims, and "being ironic" is labelled as a strategy violating the Quality Maxim. The main aim of this paper is to discuss how, by being ironic, a speaker or writer can flout not only the Maxim of Quality but the other three Gricean Maxims as well

Discourse Analysis and Pragmatics: Their Scope and Relation

In this article I delve into the seas of the disciplines of Discourse Analysis and Pragmatics, trying to perform the difficult task of delimiting their scope and discussing their common and non-common ground, in order to present a general idea of the state of the art of both disciplines in the 21st century. Being conscious of the fact that one can learn a great deal about any field by observing what its practitioners do, and precisely because these disciplines are hard to delimit, I also discuss what it is that pragmaticians and discourse analysts actually do. The concepts of text and discourse are explored by looking into different approaches and studies in the areas of Text Linguistics and Discourse Analysis, as well as into how they have evolved from their beginnings to the present time. The main schools of Pragmatics, the Anglo-American and the European Continental (Huang 2016) are also explored, in order to compare their viewpoints and their relationship with the field of discourse analysis. As I see it, Pragmatics is not the same as, but is an indispensable source for, discourse analysis: it would be impossible to analyze any discourse without having a solid basic knowledge of pragmatic phenomena and the ways in which they work and interact (Alba-Juez, 2009: 46). I also examine some concepts and issues that are crucial for the topic of this paper, such as the concepts of context , cognition or culture, and the need to develop pragmatic awareness

Irony as Inferred Contradiction

“If we acknowledge the existence of an Irony Principle, we should also acknowledge another ‘higher-order principle’ which has the opposite effect. While irony is an apparently friendly way of being offensive (mock politeness), the type of verbal behaviour known as ‘banter’ is an offensive way of being friendly (mock impoliteness).” Geoffrey Leech, Principles of Pragmatics (1983: 144) In this work I present some theoretical considerations about what I consider to be a permanent and ever-present feature of verbal irony, namely, inferred contradiction , which has to be distinguished from plain, direct (non-inferred) contradiction as well as from indirect negation , for a contradiction which is directly expressed cannot be interpreted as ironical (since it lacks a crucial component: inference), and an indirect negation may or may not be ironic (depending on the situation), and thus cannot be considered a permanent feature of the phenomenon. In spite of the fact that many scholars have proposed different theories in order to capture the essence of this intricate and complex phenomenon, not all of them have managed to find a feature or characteristic that applies to or is found in all possible occurrences of irony. I briefly discuss the tenets of some of the best-known of these theories, namely the Classical theories (Socrates, Cicero, Quintilian), the Echoic-Mention Theory (later Echoic Theory), the Echoic Reminder Theory, the Pretence Theory and the Relevant Inappropriateness Theory, trying to show that in all the types of irony emerging from these proposals (e.g. echoic irony, pretence irony, etc.) it can be observed that the irony is triggered by inferred contradiction . The one theory that according to my view and knowledge- seems to capture its whole essence to date is Attardo’s (2000) Relevant Inappropriateness Theory, to whose proposal I adhere, but I argue at the same time that inferred contradiction is another feature of irony (which goes hand in hand with relevant inappropriateness) that should be considered in any theoretical approach to irony. I also try to show how the feature of inferred contradiction is found in all the types of verbal irony identified by different authors (e.g. Alba-Juez’s 1995 negative, positive and neutral irony, Leech’s 1983, 2014 conversational irony, etc.), and thus conclude that this is a feature of irony that should be taken into consideration as a distinguishing and identifying characteristic of the phenomenon

"No wonder" as a marker of epistemic modality and affective evaluation

Depto. de Estudios Ingleses: Lingüística y LiteraturaFac. de FilologíaTRUEMinisterio de Ciencia e Innovación (MICINN)Universidad Complutense de MadridUNED, ACTUALing research grouppu

The Evaluative Function as Part of the Hidden Pragmatic Meaning of Certain Expressions in English and Spanish

Depto. de Estudios Ingleses: Lingüística y LiteraturaFac. de FilologíaTRUEMinisterio de Ciencia e Innovación (MICINN)pu

Irony Detection in Twitter: The Role of Affective Content

© ACM 2016. This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in ACM Transactions on Internet Technology, Vol. 16. http://dx.doi.org/10.1145/2930663.[EN] Irony has been proven to be pervasive in social media, posing a challenge to sentiment analysis systems. It is a creative linguistic phenomenon where affect-related aspects play a key role. In this work, we address the problem of detecting irony in tweets, casting it as a classification problem. We propose a novel model that explores the use of affective features based on a wide range of lexical resources available for English, reflecting different facets of affect. Classification experiments over different corpora show that affective information helps in distinguishing among ironic and nonironic tweets. Our model outperforms the state of the art in almost all cases.The National Council for Science and Technology (CONACyT Mexico) has funded the research work of Delia Irazu Hernandez Farias (Grant No. 218109/313683 CVU-369616). The work of Viviana Patti was partially carried out at the Universitat Politecnica de Valencia within the framework of a fellowship of the University of Turin cofunded by Fondazione CRT (World Wide Style Program 2). The work of Paolo Rosso has been partially funded by the SomEMBED TIN2015-71147-C2-1-P MINECO research project and by the Generalitat Valenciana under the grant ALMAMATER (PrometeoII/2014/030).Hernandez-Farias, DI.; Patti, V.; Rosso, P. (2016). Irony Detection in Twitter: The Role of Affective Content. ACM Transactions on Internet Technology. 16(3):19:1-19:24. https://doi.org/10.1145/2930663S19:119:24163Rob Abbott, Marilyn Walker, Pranav Anand, Jean E. Fox Tree, Robeson Bowmani, and Joseph King. 2011. How can you say such things?!?: Recognizing disagreement in informal political argument. In Proceedings of the Workshop on Languages in Social Media (LSM’11). Association for Computational Linguistics, Stroudsburg, PA, USA, 2--11.Laura Alba-Juez and Salvatore Attardo. 2014. The evaluative palette of verbal irony. In Evaluation in Context, Geoff Thompson and Laura Alba-Juez (Eds.). John Benjamins Publishing Company, Amsterdam/ Philadelphia, 93--116.Magda B. Arnold. 1960. Emotion and Personality. Vol. 1. Columbia University Press, New York, NY.Giuseppe Attardi, Valerio Basile, Cristina Bosco, Tommaso Caselli, Felice Dell’Orletta, Simonetta Montemagni, Viviana Patti, Maria Simi, and Rachele Sprugnoli. 2015. State of the art language technologies for italian: The EVALITA 2014 perspective. Journal of Intelligenza Artificiale 9, 1 (2015), 43--61.Salvatore Attardo. 2000. Irony as relevant inappropriateness. Journal of Pragmatics 32, 6 (2000), 793--826.Stefano Baccianella, Andrea Esuli, and Fabrizio Sebastiani. 2010. SentiWordNet 3.0: An enhanced lexical resource for sentiment analysis and opinion mining. In Proceedings of the 7th International Conference on Language Resources and Evaluation (LREC’10). European Language Resources Association (ELRA), Valletta, Malta, 2200,2204.David Bamman and Noah A. Smith. 2015. Contextualized sarcasm detection on twitter. In Proceedings of the 9th International Conference on Web and Social Media, (ICWSM’15). AAAI, Oxford, UK, 574--577.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. Association for Computational Linguistics, Baltimore, Maryland, 50--58.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). Pisa University Press, Pisa, Italy, 50--57.Cristina Bosco, Viviana Patti, and Andrea Bolioli. 2013. Developing corpora for sentiment analysis: The case of irony and senti-TUT. IEEE Intelligent Systems 28, 2 (March 2013), 55--63.Andrea Bowes and Albert Katz. 2011. When sarcasm stings. Discourse Processes: A Multidisciplinary Journal 48, 4 (2011), 215--236.Margaret M. Bradley and Peter J. Lang. 1999. Affective Norms for English Words (ANEW): Instruction Manual and Affective Ratings. Technical Report. Center for Research in Psychophysiology, University of Florida, Gainesville, Florida.Konstantin Buschmeier, Philipp Cimiano, and Roman Klinger. 2014. An impact analysis of features in a classification approach to irony detection in product reviews. In Proceedings of the 5th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis. Association for Computational Linguistics, Baltimore, Maryland, 42--49.Erik Cambria, Andrew Livingstone, and Amir Hussain. 2012. The hourglass of emotions. In Cognitive Behavioural Systems. Lecture Notes in Computer Science, Vol. 7403. Springer, Berlin, 144--157.Erik Cambria, Daniel Olsher, and Dheeraj Rajagopal. 2014. SenticNet 3: A common and common-sense knowledge base for cognition-driven sentiment analysis. In Proceedings of AAAI Conference on Artificial Intelligence. AAAI, Québec, Canada, 1515--1521.Jorge Carrillo de Albornoz, Laura Plaza, and Pablo Gervás. 2012. SentiSense: An easily scalable concept-based affective lexicon for sentiment analysis. In Proceedings of the 8th International Conference on Language Resources and Evaluation (LREC’12) (23-25), Nicoletta Calzolari (Conference Chair), Khalid Choukri, Thierry Declerck, Mehmet Ugur Dogan, Bente Maegaard, Joseph Mariani, Asuncion Moreno, Jan Odijk, and Stelios Piperidis (Eds.). European Language Resources Association (ELRA), Istanbul, Turkey, 3562--3567.Paula Carvalho, Luís Sarmento, Mário J. Silva, and Eugénio de Oliveira. 2009. Clues for detecting irony in user-generated contents: Oh&hallip;!! It’s “so easy” ;-). In Proceedings of the 1st International CIKM Workshop on Topic-sentiment Analysis for Mass Opinion (TSA’09). ACM, New York, NY, 53--56.Yoonjung Choi and Janyce Wiebe. 2014. +/-EffectWordNet: Sense-level lexicon acquisition for opinion inference. In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP’14). Association for Computational Linguistics, Doha, Qatar, 1181--1191.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). Association for Computational Linguistics, Uppsala, Sweden, 107--116.Shelly Dews, Joan Kaplan, and Ellen Winner. 1995. Why not say it directly? The social functions of irony. Discourse Processes 19, 3 (1995), 347--367.Paul Ekman. 1992. An argument for basic emotions. Cognition and Emotion 6, 3--4 (1992), 169--200.Elisabetta Fersini, Federico Alberto Pozzi, and Enza Messina. 2015. Detecting irony and sarcasm in microblogs: The role of expressive signals and ensemble classifiers. In 2015 IEEE International Conference on Data Science and Advanced Analytics (DSAA’15). IEEE Xplore Digital Library, Paris, France, 1--8.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), Istanbul, 392--398.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). Association for Computational Linguistics, Denver, Colorado, 470--478.Raymond W. Gibbs. 2000. Irony in talk among friends. Metaphor and Symbol 15, 1--2 (2000), 5--27.Rachel Giora and Salvatore Attardo. 2014. Irony. In Encyclopedia of Humor Studies. SAGE, Thousand Oaks, CA.Rachel Giora and Ofer Fein. 1999. Irony: Context and salience. Metaphor and Symbol 14, 4 (1999), 241--257.Roberto González-Ibáñez, Smaranda Muresan, and Nina Wacholder. 2011. Identifying sarcasm in twitter: A closer look. In Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies (HLT’11). Association for Computational Linguistics, Portland, OR, 581--586.H. Paul Grice. 1975. Logic and conversation. In Syntax and Semantics: Vol. 3: Speech Acts, P. Cole and J. L. Morgan (Eds.). Academic Press, San Diego, CA, 41--58.Irazú Hernández Farías, José-Miguel Benedí, and Paolo Rosso. 2015. Applying basic features from sentiment analysis for automatic irony detection. In Pattern Recognition and Image Analysis. Lecture Notes in Computer Science, Vol. 9117. Springer International Publishing, Santiago de Compostela, Spain, 337--344.Minqing Hu and Bing Liu. 2004. Mining and summarizing customer reviews. In Proceedings of the 10th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD’04). ACM, Seattle, WA, 168--177.Aditya Joshi, Vinita Sharma, and Pushpak Bhattacharyya. 2015. Harnessing context incongruity for sarcasm detection. In Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 2: Short Papers). Association for Computational Linguistics, Beijing, China, 757--762.Jihen Karoui, Farah Benamara, Véronique Moriceau, Nathalie Aussenac-Gilles, and Lamia Hadrich-Belguith. 2015. Towards a contextual pragmatic model to detect irony in tweets. In Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 2: Short Papers). Association for Computational Linguistics, Beijing, China, 644--650.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 (FigLanguages’07). Association for Computational Linguistics, Rochester, NY, 1--4.Florian Kunneman, Christine Liebrecht, Margot van Mulken, and Antal van den Bosch. 2015. Signaling sarcasm: From hyperbole to hashtag. Information Processing & Management 51, 4 (2015), 500--509.Christopher Lee and Albert Katz. 1998. The differential role of ridicule in sarcasm and irony. Metaphor and Symbol 13, 1 (1998), 1--15.John S. Leggitt and Raymond W. Gibbs. 2000. Emotional reactions to verbal irony. Discourse Processes 29, 1 (2000), 1--24.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 Analysis in Social Media. Association for Computational Linguistics, Atlanta, GA, 30--40.Diana Maynard and Mark 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) (26-31). European Language Resources Association (ELRA), Reykjavik, Iceland, 4238--4243.Skye McDonald. 2007. Neuropsychological studies of sarcasm. In Irony in Language and Thought: A Cognitive Science Reader, H. Colston and R. Gibbs (Eds.). Lawrence Erlbaum, 217--230.Saif M. Mohammad and Peter D. Turney. 2013. Crowdsourcing a word--emotion association lexicon. Computational Intelligence 29, 3 (2013), 436--465.Saif M. Mohammad, Xiaodan Zhu, Svetlana Kiritchenko, and Joel Martin. 2015. Sentiment, emotion, purpose, and style in electoral tweets. Information Processing & Management 51, 4 (2015), 480--499.Finn Årup Nielsen. 2011. A new ANEW: Evaluation of a word list for sentiment analysis in microblogs. In Proceedings of the ESWC2011 Workshop on “Making Sense of Microposts”: Big Things Come in Small Packages (CEUR Workshop Proceedings), Vol. 718. CEUR-WS.org, Heraklion, Crete, Greece, 93--98.W. Gerrod Parrot. 2001. Emotions in Social Psychology: Essential Readings. Psychology Press, Philadelphia, PA.James W. Pennebaker, Martha E. Francis, and Roger J. Booth. 2001. Linguistic Inquiry and Word Count: LIWC 2001. Mahway: Lawrence Erlbaum Associates, 71.Robert Plutchik. 2001. The nature of emotions. American Scientist 89, 4 (2001), 344--350.Soujanya Poria, Alexander Gelbukh, Amir Hussain, Newton Howard, Dipankar Das, and Sivaji Bandyopadhyay. 2013. Enhanced senticnet with affective labels for concept-based opinion mining. IEEE Intelligent Systems 28, 2 (2013), 31--38.Tomáš Ptáček, 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). Dublin City University and Association for Computational Linguistics, Dublin, Ireland, 213--223.Ashwin Rajadesingan, Reza Zafarani, and Huan Liu. 2015. Sarcasm detection on twitter: A behavioral modeling approach. In Proceedings of the 8th ACM International Conference on Web Search and Data Mining (WSDM’15). ACM, 97--106.Antonio Reyes and Paolo Rosso. 2014. On the difficulty of automatically detecting irony: Beyond a simple case of negation. Knowledge Information Systems 40, 3 (2014), 595--614.Antonio Reyes, Paolo Rosso, and Tony Veale. 2013. A multidimensional approach for detecting irony in twitter. Language Resources and Evaluation 47, 1 (2013), 239--268.Ellen Riloff, Ashequl Qadir, Prafulla Surve, Lalindra De Silva, Nathan Gilbert, and Ruihong Huang. 2013. Sarcasm as contrast between a positive sentiment and negative situation. In Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing, (EMNLP’13). Association for Computational Linguistics, Seattle, Washington, 704--714.Simone Shamay-Tsoory, Rachel Tomer, B. D. Berger, Dorith Goldsher, and Judith Aharon-Peretz. 2005. Impaired “affective theory of mind” is associated with right ventromedial prefrontal damage. Cognitive Behavioral Neurology 18, 1 (2005), 55--67.Philip J. Stone and Earl B. Hunt. 1963. A computer approach to content analysis: Studies using the general inquirer system. In Proceedings of the May 21-23, 1963, Spring Joint Computer Conference (AFIPS’63 (Spring)). ACM, New York, NY, 241--256.Emilio Sulis, Delia Irazú Hernández Farías, Paolo Rosso, Viviana Patti, and Giancarlo Ruffo. 2016. Figurative messages and affect in Twitter: Differences between #irony, #sarcasm and #not. Knowledge-Based Systems. In Press. Available online.Maite Taboada and Jack Grieve. 2004. Analyzing appraisal automatically. In Proceedings of the AAAI Spring Symposium on Exploring Attitude and Affect in Text: Theories and Applications. AAAI, Stanford, CA, 158--161.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). Association for Computational Linguistics, Dublin, Ireland, 1269--1278.Tony Veale and Yanfen Hao. 2010. Detecting ironic intent in creative comparisons. In Proceedings of the 19th European Conference on Artificial Intelligence. IOS Press, Amsterdam, The Netherlands, 765--770.Byron C. Wallace. 2015. Computational irony: A survey and new perspectives. Artificial Intelligence Review 43, 4 (2015), 467--483.Byron C. Wallace, Do Kook Choe, and Eugene Charniak. 2015. Sparse, contextually informed models for irony detection: Exploiting user communities, entities and sentiment. In Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). Association for Computational Linguistics, Beijing, China, 1035--1044.Angela P. 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 Computation (PACLIC’13). Department of English, National Chengchi University, Taipei, Taiwan, 349--356.Juanita M. Whalen, Penny M. Pexman, J. Alastair Gill, and Scott Nowson. 2013. Verbal irony use in personal blogs. Behaviour & Information Technology 32, 6 (2013), 560--569.Cynthia Whissell. 2009. Using the revised dictionary of affect in language to quantify the emotional undertones of samples of natural languages. Psychological Reports 2, 105 (2009), 509--521.Deirdre Wilson and Dan Sperber. 1992. On verbal irony. Lingua 87, 1--2 (1992), 53--76.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’05). Association for Computational Linguistics, Stroudsburg, PA, 347--354.Alecia Wolf. 2000. Emotional expression online: Gender differences in emoticon use. CyberPsychology & Behavior 3, 5 (2000), 827--833.Zhibiao Wu and Martha Palmer. 1994. Verbs semantics and lexical selection. In Proceedings of the 32nd Annual Meeting on Association for Computational Linguistics (ACL’94). Association for Computational Linguistics, Stroudsburg, PA, 133--138