Irony and Sarcasm Detection in Twitter: The Role of Affective Content

PhD thesis in Computer Science written by Delia Irazú Hernández Faŕıas under the supervision of Dr. Paolo Rosso (Universitat Politècnica de València) and Dra. Viviana Patti (University of Turin). This thesis was developed under a cotutelle between the Universitat Politècnica de València, Spain and the University of Turin, Italy. The thesis defense was done in Valencia, Spain on September 25, 2017. The doctoral committee was integrated by: Horacio Saggion (Universitat Pompeu Fabra), Elisabetta Fersini (Università degli Studi di Milano-Bicocca), and Roberto Basili (Univerità di Roma Tor Vergata). The International mention was achieved after a 12 months internship at University of Turin.

[1]  Chu-Ren Huang,et al.  LLT-PolyU: Identifying Sentiment Intensity in Ironic Tweets , 2015, SemEval@NAACL-HLT.

[2]  Lei Zhang,et al.  Sentiment Analysis and Opinion Mining , 2017, Encyclopedia of Machine Learning and Data Mining.

[3]  Elena Filatova,et al.  Irony and Sarcasm: Corpus Generation and Analysis Using Crowdsourcing , 2012, LREC.

[4]  Nicole Novielli,et al.  UNIBA at EVALITA 2014-SENTIPOLC Task: Predicting tweet sentiment polarity combining micro-blogging, lexicon and semantic features , 2014 .

[5]  Ines Gloeckner,et al.  Relevance Communication And Cognition , 2016 .

[6]  Finn Årup Nielsen,et al.  A New ANEW: Evaluation of a Word List for Sentiment Analysis in Microblogs , 2011, #MSM.

[7]  Raymond W. Gibbs,et al.  Emotional Reactions to Verbal Irony , 2000 .

[8]  Susan D. Wiley,et al.  The effects of mindfulness-based stress reduction on nurse stress and burnout, Part II: A quantitative and qualitative study. , 2004, Holistic nursing practice.

[9]  R. Kreuz,et al.  Lexical Influences on the Perception of Sarcasm , 2007 .

[10]  Sunghwan Mac Kim,et al.  EMOTIONS IN TEXT: DIMENSIONAL AND CATEGORICAL MODELS , 2013, Comput. Intell..

[11]  Roger Sauter,et al.  Introduction to Probability and Statistics for Engineers and Scientists , 2005, Technometrics.

[12]  Véronique Hoste,et al.  Monday mornings are my fave : ) #not Exploring the Automatic Recognition of Irony in English tweets , 2016, COLING.

[13]  Penny M. Pexman,et al.  Children's Perceptions of the Social Functions of Verbal Irony , 2003 .

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

[15]  Antal van den Bosch,et al.  Signaling sarcasm: From hyperbole to hashtag , 2015, Inf. Process. Manag..

[16]  Carmen Curcó Irony: Negation, echo and metarepresentation , 2000 .

[17]  Bing Liu,et al.  Mining and summarizing customer reviews , 2004, KDD.

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

[19]  I. Poggi,et al.  Multimodal markers of irony and sarcasm , 2003 .

[20]  Erik Cambria,et al.  SenticNet 3: A Common and Common-Sense Knowledge Base for Cognition-Driven Sentiment Analysis , 2014, AAAI.

[21]  Mohammad Ali Abbasi,et al.  Real-World Behavior Analysis through a Social Media Lens , 2012, SBP.

[22]  Andrea Esuli,et al.  SentiWordNet 3.0: An Enhanced Lexical Resource for Sentiment Analysis and Opinion Mining , 2010, LREC.

[23]  Preslav Nakov,et al.  SemEval-2015 Task 10: Sentiment Analysis in Twitter , 2015, *SEMEVAL.

[24]  Byron C. Wallace Computational irony: A survey and new perspectives , 2013, Artificial Intelligence Review.

[25]  Erik Cambria,et al.  Merging SenticNet and WordNet-Affect emotion lists for sentiment analysis , 2012, 2012 IEEE 11th International Conference on Signal Processing.

[26]  Joel D. Martin,et al.  Sentiment, emotion, purpose, and style in electoral tweets , 2015, Inf. Process. Manag..

[27]  Tony Veale,et al.  Fracking Sarcasm using Neural Network , 2016, WASSA@NAACL-HLT.

[28]  Davide Buscaldi,et al.  IRADABE: Adapting English Lexicons to the Italian Sentiment Polarity Classification task , 2014 .

[29]  Maite Taboada,et al.  Analyzing Appraisal Automatically , 2004 .

[30]  Scott Nowson,et al.  Verbal irony use in personal blogs , 2013, Behav. Inf. Technol..

[31]  C. Whissell Using the Revised Dictionary of Affect in Language to Quantify the Emotional Undertones of Samples of Natural Language , 2009, Psychological reports.

[32]  A. Utsumi Verbal irony as implicit display of ironic environment: Distinguishing ironic utterances from nonirony☆ , 2000 .

[33]  P. Ekman An argument for basic emotions , 1992 .

[34]  Tamar Fraenkel,et al.  The meaning of negated adjectives , 2008 .

[35]  Dipankar Das,et al.  Enhanced SenticNet with Affective Labels for Concept-Based Opinion Mining , 2013, IEEE Intelligent Systems.

[36]  Horacio Saggion,et al.  UPF-taln: SemEval 2015 Tasks 10 and 11. Sentiment Analysis of Literal and Figurative Language in Twitter , 2015, SemEval@NAACL-HLT.

[37]  Rodolfo Delmonte ITGETARUNS A Linguistic Rule-Based System for Pragmatic Text Processing , 2014 .

[38]  E. Winner,et al.  Why not say it directly? The social functions of irony , 1995 .

[39]  Dipankar Das,et al.  Enriching SenticNet Polarity Scores through Semi-Supervised Fuzzy Clustering , 2012, 2012 IEEE 12th International Conference on Data Mining Workshops.

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

[41]  Pushpak Bhattacharyya,et al.  Your Sentiment Precedes You: Using an author’s historical tweets to predict sarcasm , 2015, WASSA@EMNLP.

[42]  J Aharon-Peretz,et al.  Impaired “Affective Theory of Mind” Is Associated with Right Ventromedial Prefrontal Damage , 2005, Cognitive and behavioral neurology : official journal of the Society for Behavioral and Cognitive Neurology.

[43]  Hinrich Schütze,et al.  Book Reviews: Foundations of Statistical Natural Language Processing , 1999, CL.

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

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

[46]  Martha Palmer,et al.  Verb Semantics and Lexical Selection , 1994, ACL.

[47]  Pablo Gervás,et al.  SentiSense: An easily scalable concept-based affective lexicon for sentiment analysis , 2012, LREC.

[48]  Marilyn A. Walker,et al.  Really? Well. Apparently Bootstrapping Improves the Performance of Sarcasm and Nastiness Classifiers for Online Dialogue , 2013, ArXiv.

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

[50]  Tony Veale,et al.  Detecting Ironic Intent in Creative Comparisons , 2010, ECAI.

[51]  Pushpak Bhattacharyya,et al.  Automatic Sarcasm Detection: A Survey , 2016 .

[52]  Horacio Saggion,et al.  Modelling Irony in Twitter , 2014, EACL.

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

[54]  Ferran Plà,et al.  ELiRF: A Support Vector Machine Approach for Sentiment Analysis Tasks in Twitter at SemEval-2015 , 2015 .

[55]  Nathalie Aussenac-Gilles,et al.  Exploring the Impact of Pragmatic Phenomena on Irony Detection in Tweets: A Multilingual Corpus Study , 2017, EACL.

[56]  Elisabetta Fersini,et al.  Unsupervised Irony Detection: A Probabilistic Model with Word Embeddings , 2016, KDIR.

[57]  Herbert L. Colston “Not good” is “bad,” but “not bad” is not “good”: An analysis of three accounts of negation asymmetry , 1999 .

[58]  Rachel Giora,et al.  Default Sarcastic Interpretations: On the Priority of Nonsalient Interpretations , 2015 .

[59]  Véronique Hoste,et al.  LT3: Sentiment Analysis of Figurative Tweets: piece of cake #NotReally , 2015, SemEval@NAACL-HLT.

[60]  Roger J. Kreuz,et al.  The empirical study of figurative language in literature , 1993 .

[61]  Mário J. Silva,et al.  Clues for detecting irony in user-generated contents: oh...!! it's "so easy" ;-) , 2009, TSA@CIKM.

[62]  Debanjan Ghosh,et al.  Sarcastic or Not: Word Embeddings to Predict the Literal or Sarcastic Meaning of Words , 2015, EMNLP.

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

[64]  Saif Mohammad,et al.  CROWDSOURCING A WORD–EMOTION ASSOCIATION LEXICON , 2013, Comput. Intell..

[65]  W. G. Parrott,et al.  Emotions in social psychology : essential readings , 2001 .

[66]  Amy Beth Warriner,et al.  Norms of valence, arousal, and dominance for 13,915 English lemmas , 2013, Behavior Research Methods.

[67]  Marta Dynel,et al.  Linguistic approaches to (non)humorous irony , 2014 .

[68]  Elisabetta Fersini,et al.  Subjectivity , Polarity And Irony Detection : A Multi-Layer Approach , 2014 .

[69]  Davide Buscaldi,et al.  From humor recognition to irony detection: The figurative language of social media , 2012, Data Knowl. Eng..

[70]  Preslav Nakov,et al.  SemEval-2016 Task 4: Sentiment Analysis in Twitter. , 2019 .

[71]  R. Gibbs,et al.  Psychological aspects of irony understanding , 1991 .

[72]  Albert Katz,et al.  When Sarcasm Stings , 2011 .

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

[74]  Joan Lucariello Situational irony: A concept of events gone awry. , 1994 .

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

[76]  Laura Alba Juez Irony and the other off record strategies within politeness theory , 1995 .

[77]  Ofer Fein,et al.  Defaultness Reigns: The Case of Sarcasm , 2015 .

[78]  Preslav Nakov,et al.  SemEval-2014 Task 9: Sentiment Analysis in Twitter , 2014, *SEMEVAL.

[79]  Saif Mohammad,et al.  NRC-Canada: Building the State-of-the-Art in Sentiment Analysis of Tweets , 2013, *SEMEVAL.

[80]  James W. Pennebaker,et al.  Linguistic Inquiry and Word Count (LIWC2007) , 2007 .

[81]  Erik Cambria,et al.  Sentic Computing: A Common-Sense-Based Framework for Concept-Level Sentiment Analysis , 2015 .

[82]  Jacob L. Mey,et al.  The nature of irony: Toward a computational model of irony , 1991 .

[83]  Peng Liu,et al.  Sarcasm Detection in Social Media Based on Imbalanced Classification , 2014, WAIM.

[84]  H. Kotthoff,et al.  Gender and joking: On the complexities of women's image politics in humorous narratives , 2000 .

[85]  Rossano Schifanella,et al.  Detecting Sarcasm in Multimodal Social Platforms , 2016, ACM Multimedia.

[86]  Ofer Fein,et al.  The Role of Defaultness in Affecting Pleasure: The Optimal Innovation Hypothesis Revisited , 2017 .

[87]  Ted Pedersen,et al.  WordNet::Similarity - Measuring the Relatedness of Concepts , 2004, NAACL.

[88]  Paolo Rosso,et al.  Applying Basic Features from Sentiment Analysis for Automatic Irony Detection , 2015, IbPRIA.

[89]  Hartmut Leuthold,et al.  When language gets emotional: irony and the embodiment of affect in discourse. , 2015, Acta psychologica.

[90]  Ian H. Witten,et al.  The WEKA data mining software: an update , 2009, SKDD.

[91]  Ofer Fein,et al.  Irony: Context and Salience , 1999 .

[92]  Shigehiro Haruki,et al.  Jocularity in irony and humor : A cognitive-to-affective process , 2000 .

[93]  Paolo Rosso,et al.  Distinguishing between irony and sarcasm in social media texts: Linguistic observations , 2016, 2016 International FRUCT Conference on Intelligence, Social Media and Web (ISMW FRUCT).

[94]  Tommaso Caselli,et al.  State of the Art Language Technologies for Italian: The EVALITA 2014 Perspective , 2015, Intelligenza Artificiale.

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

[96]  Laura Alba Juez,et al.  The evaluative palette of verbal irony , 2014 .

[97]  S. Attardo Irony as relevant inappropriateness , 2000 .

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

[99]  Mathieu Bastian,et al.  Gephi: An Open Source Software for Exploring and Manipulating Networks , 2009, ICWSM.

[100]  Pushpak Bhattacharyya,et al.  Are Word Embedding-based Features Useful for Sarcasm Detection? , 2016, EMNLP.

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

[102]  Erik Cambria,et al.  The Hourglass of Emotions , 2011, COST 2102 Training School.

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

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

[105]  J. Russell,et al.  Evidence for a three-factor theory of emotions , 1977 .

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

[107]  Reza Zafarani,et al.  Sarcasm Detection on Twitter: A Behavioral Modeling Approach , 2015, WSDM.

[108]  Saif M. Mohammad,et al.  Sentiment Analysis: Detecting Valence, Emotions, and Other Affectual States from Text , 2016, ArXiv.

[109]  Janyce Wiebe,et al.  Recognizing Contextual Polarity in Phrase-Level Sentiment Analysis , 2005, HLT.

[110]  Erik Cambria,et al.  A Deeper Look into Sarcastic Tweets Using Deep Convolutional Neural Networks , 2016, COLING.

[111]  Horacio Saggion,et al.  Modelling Sarcasm in Twitter, a Novel Approach , 2014, WASSA@ACL.

[112]  Ofer Fein,et al.  Negation Generates Nonliteral Interpretations by Default , 2013 .

[113]  Paolo Rosso,et al.  On the impact of emotions on author profiling , 2016, Inf. Process. Manag..

[114]  Byron C. Wallace,et al.  Humans Require Context to Infer Ironic Intent (so Computers Probably do, too) , 2014, ACL.

[115]  Benno Stein,et al.  Overview of the 2 nd Author Profiling Task at PAN 2014 , 2014 .

[116]  M. Walker,et al.  How can you say such things?!?: Recognizing Disagreement in Informal Political Argument , 2011 .

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

[118]  Dirk Hovy,et al.  Putting Sarcasm Detection into Context: The Effects of Class Imbalance and Manual Labelling on Supervised Machine Classification of Twitter Conversations , 2016, ACL.

[119]  Alecia Wolf,et al.  Emotional Expression Online: Gender Differences in Emoticon Use , 2000, Cyberpsychology Behav. Soc. Netw..

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

[121]  Sabine Bergler,et al.  A Comparative Study of Different Sentiment Lexica for Sentiment Analysis of Tweets , 2015, RANLP.

[122]  Skye McDonald,et al.  Neuropsychological Studies of Sarcasm , 2000 .

[123]  M. Jacomy,et al.  ForceAtlas2, a Continuous Graph Layout Algorithm for Handy Network Visualization Designed for the Gephi Software , 2014, PloS one.

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

[125]  Janyce Wiebe,et al.  +/-EffectWordNet: Sense-level Lexicon Acquisition for Opinion Inference , 2014, EMNLP.

[126]  Paolo Rosso,et al.  On the difficulty of automatically detecting irony: beyond a simple case of negation , 2014, Knowledge and Information Systems.

[127]  P. Wilson,et al.  The Nature of Emotions , 2012 .

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

[129]  Erik Cambria,et al.  EmoSenticSpace: A novel framework for affective common-sense reasoning , 2014, Knowl. Based Syst..

[130]  Paolo Rosso,et al.  Sentiment Polarity Classification of Figurative Language: Exploring the Role of Irony-Aware and Multifaceted Affect Features , 2017, CICLing.

[131]  M. Bradley,et al.  Affective Norms for English Words (ANEW): Instruction Manual and Affective Ratings , 1999 .

[132]  S. Glucksberg,et al.  How about another piece of pie: the allusional pretense theory of discourse irony. , 1995, Journal of experimental psychology. General.

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

[134]  Elisabetta Fersini,et al.  Detecting irony and sarcasm in microblogs: The role of expressive signals and ensemble classifiers , 2015, 2015 IEEE International Conference on Data Science and Advanced Analytics (DSAA).

[135]  Philip J. Stone,et al.  A computer approach to content analysis: studies using the General Inquirer system , 1963, AFIPS Spring Joint Computing Conference.

[136]  Peter D. Turney Thumbs Up or Thumbs Down? Semantic Orientation Applied to Unsupervised Classification of Reviews , 2002, ACL.

[137]  Ellen Riloff,et al.  Sarcasm as Contrast between a Positive Sentiment and Negative Situation , 2013, EMNLP.

[138]  Paolo Rosso,et al.  Irony, Sarcasm, and Sentiment Analysis , 2017 .

[139]  Sabine Bergler,et al.  CLaC-SentiPipe: SemEval2015 Subtasks 10 B,E, and Task 11 , 2015, *SEMEVAL.

[140]  Po-Ya Angela Wang #Irony or #Sarcasm — A Quantitative and Qualitative Study Based on Twitter , 2013, PACLIC.