References

  1. Abdaoui, A., Tapi Nzali, M.D., Azé, J., Bringay, S., Lavergne, C., Mollevi C., Poncelet, P. (2015). Advanse: analyse du sentiment, de l’opinion et de l’émotion sur des tweets français. Actes du 11e Défi Fouille de Texte, Caen, France, 78–87. Available at: http://www.atala.org/taln_archives/DEFT/DEFT-2015/deft-2015-long-009.
  2. Abdul-Mageed, M., Diab, M., Kübler, S. (2014). Samar: subjectivity and sentiment analysis for arabic social media. Computer Speech & Language, 28(1), 20–37.
  3. Abouenour, L., Bouzoubaa, K., Rosso, P. (2012). Idraaq: new arabic question answering system based on query expansion and passage retrieval. CELCT 2012. Available at: https://pdfs.semanticscholar.org/650c/6525691136c50b312710662bb7c7b0da2bba.pdf.
  4. Afify, M., Sarikaya, R., Kuo, H.-K.J., Besacier, L., Gao, Y. (2006). On the use of morphological analysis for dialectal arabic speech recognition. INTERSPEECH 2006 – ICSLP, Pittsburgh, PA.
  5. Alorifi, F.S. (2008). Automatic identification of arabic dialects using hidden markov models. Doctorate Thesis, Université de Pittsburgh, Pittsburgh, PA.
  6. Al-Sughaiyer, I.A., Al-Kharashi, I.A. (2004). Arabic morphological analysis techniques: a comprehensive survey. Journal of the American Society for Information Science and Technology (JASIST), 55(3), 189–213.
  7. Angenot, M. (1982). La parole pamphlétaire. Payot, Paris.
  8. Asher, N., Benamara, F., Mathieu, Y.Y. (2009). Appraisal of opinion expressions in discourse. Lingvisticæ Investigationes, 32(2), 279–292.
  9. Attardo, S. (1994). Linguistic Theories of Humor. Walter de Gruyter, Berlin.
  10. Attardo, S. (2000a). Irony as relevant in appropriateness. Journal of Pragmatics, 32(6), 793–826.
  11. Attardo, S. (2000b). Irony markers and functions: towards a goal-oriented theory of irony and its processing. Rask – International journal of Language and Communication, 12(1), 3–20.
  12. Attardo, S. (2001). Humorous texts: a semantic and pragmatic analysis. Walter de Gruyter, Berlin.
  13. Azé, J., Roche, M. (2005). Présentation de l’atelier DEFT’05. Proceedings of TALN 2005 – Atelier DEFT’05, Dourdan, France 2, 99–111.
  14. Bahou, Y., Masmoudi, A., Hadrich Belguith, L. (2010). Traitement des disfluences dans le cadre de la compréhension automatique de l’oral arabe spontané. Actes de la 17e conférence sur le traitement automatique des langues naturelles, association pour le traitement automatique des langues, Montreal, Canada. Available at: http://www.atala.org/taln_archives/TALN/TALN-2010/taln-2010-long-021.
  15. Baker, C.F., Fillmore, C.J., Cronin, B. (2003). The structure of the frame net database. International Journal of Lexicography, 16(3), 281–296.
  16. Bamman, D., Smith, N.A. (2015). Contextualized sarcasm detection on Twitter. Proceedings of the 9th International AAAI Conference on Web and Social Media, Oxford, UK, 574–577.
  17. Barbe, K. (1995). Irony in Context. John Benjamins Publishing, Amsterdam.
  18. Barbieri, F., Saggion, H. (2014a). Modelling irony in Twitter. Proceedings of the Student Research Workshop at the 14th Conference of the European Chapter of the Association for Computational Linguistics (EACL 2014), Gothenburg, Sweden, 56–64.
  19. Barbieri, F., Saggion, H. (2014b). Modelling irony in Twitter: feature analysis and evaluation. Proceedings of the 9th International Conference on Language Resources and Evaluation (LREC 2014), Reykjavik, Iceland, 4258–4264.
  20. Barbieri, F., Saggion, H., Ronzano, F. (2014). Modelling sarcasm in Twitter, a novel approach. Proceedings of the 5th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis, Baltimore, MD, 50–58.
  21. Basile, V., Nissim, M. (2013). Sentiment analysis on Italian tweets. Proceedings of the 4th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis, Atlanta, GA, 100–107.
  22. Basile, V., Bolioli, A., Nissim, M., Patti, V., Rosso, P. (2014). Overview of the Evalita 2014 SENTIment POLarity Classification Task. Proceedings of the 4th evaluation campaign of Natural Language Processing and Speech tools for Italian (EVALITA’14), Pisa University Press, Pisa, Italy, 50–57.
  23. Bastien, F., Lamblin, P., Pascanu, R., Bergstra, J., Goodfellow, I., Bergeron, A., Bouchard, N., Warde-Farley, D., Bengio, Y. (2012). Theano: new features and speed improvements. Cornell University, Ithaca.
  24. Bautain, L. (1816). De la satire. De l’Imprimerie De C.-F. Patris, Paris.
  25. Bdour, W.N., Gharaibeh, N.K. (2013). Development of yes/no arabic question answering system. International Journal of Artificial Intelligence & Applications, 4(1), 51–63.
  26. Benamara, F. (2017). Analyse automatique d’opinions états des lieux et perspectives. Techniques de l’ingénieur. Représentation et traitement des documents numériques. Available at: http://www.techniques-ingenieur.fr/base-documentaire/technologies-de-l-information-th9/representation-et-traitement-des-documents-numeriques-42312210/analyse-automatique-d-opinions-h7270/.
  27. Benamara, F., Moriceau, V., Mathieu, Y.Y. (2014). Catégorisation sémantique fine des expressions d’opinion pour la détection de consensus, Actes du 10e Défi Fouille de Textes, Marseille, France, 36–44.
  28. Benamara, F., Asher, N., Mathieu, Y., Popescu, V., Chardon, B. (2016). Evaluation in discourse: a corpus-based study. Dialogue and Discourse, 7(1), 1–49.
  29. Benamara, F., Grouin, C., Karoui, J., Moriceau, V., Robba, I. (2017a). Analyse d’opinion et langage figuratif dans des tweets: présentation et résultats du Défi Fouille de Textes DEFT2017. Actes du 13e Défi Fouille de Textes, Orléans, France. Available at: https://deft.limsi.fr/2017/actes_DEFT_2017.pdf.
  30. Benamara, F., Taboada, M., Mathieu, Y.Y. (2017b). Evaluative language beyond bags of words: linguistic insights and computational applications. Computational Linguistics, 43(1), 201–264.
  31. Bentivogli, L., Forner, P., Magnini, B., Pianta, E. (2004). Revising the Wordnet Domains Hierarchy: semantics, coverage and balancing. Proceedings of the Workshop on Multilingual Linguistic Resources, Geneva, Switzerland, 101–108.
  32. Berntsen, D., Kennedy, J.M. (1996). Unresolved contradictions specifying attitude sinmetaphor, irony, understatement and tautology. Poetics, 24(1), 13–29.
  33. Bertero, D., Fung, P. (2016). Along short-term memory framework for predicting humor in dialogues. 15th Annual Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, San Diego, CA.
  34. Bertero, D., Fung, P. (2016). Deep learning of audio and language features for humor prediction. Journal of Lightwave Technology, Portorož, Slovenia, 496–501.
  35. Bestgen, Y., Cabiaux, A.-F. (2002). L’analyse sémantique latente et l’identification des métaphores. 6e Rencontre des étudiants chercheurs en informatique pour le traitement automatique des langues, Nancy, France, 331–337.
  36. Bestgen, Y., Lories, G. (2009). Un niveau de base pour la tâche 1 (corpus français et anglais) de DEFT’09. Actes du 5e Défi Fouille de Textes, Paris, France.
  37. Biadsy, F., Hirschberg, J., Habash, N. (2009). Spoken arabic dialect identification using phonotactic modeing. Proceedings of the EACL 2009 Workshop on Computational Approaches to Semitic Languages, Athens, Greece, 53–61.
  38. Bosco, C., Patti, V., Bolioli, A. (2013). Developing corpora for sentiment analysis: the case of irony and senti-tut. IEEE Intelligent Systems, 28(2), 55–63.
  39. Boyd, R. (1979). Metaphor and theory change: what is “metaphor” a metaphor for? In Metaphor and Thought, Ortony, A. (ed.). Cambridge University Press, Cambridge.
  40. Bres, J. (2010). L’ironie, un cocktail dialogique? 2e Congrès mondial de linguistique française, New Orleans, LA.
  41. Burfoot, C., Baldwin, C. (2009). Automatic satire detection: Are you having a laugh? Proceedings of the ACL-IJCNLP 2009 Conference Short Papers, Suntec, Singapore, 161–164.
  42. Burgers, C. (2010). Verbal irony: use and effects in written discourse. Doctorate Thesis, Radboud Universiteit Nijmegen, Nijmegen, The Netherlands.
  43. Buschmeier, K., Cimiano, P., Klinger, R. (2014). An impact analysis of features in a classification approach to irony detection in product reviews. Proceedings of the 5th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis, Baltimore, MD, 42–49.
  44. Campigotto, R., Conde Céspedes, P., Guillaume, J.-L. (2014). La méthode de Louvain générique: un algorithme adaptatif pour la détection de communautés sur de très grands graphes. ROADEF – 15e congrès annuel de la Société française de recherche opérationnelle et d’aide à la décision. Bordeaux, France. Available at: https://hal.archives-ouvertes.fr/hal-00946481.
  45. Carpuat, M., Marton, Y., Habash, N. (2012). Improved arabic-to-english statistical machine translation by reordering post-verbal subjects for word alignment. Machine Translation, 26(1–2), 105–120.
  46. Carvalho, P., Sarmento, L., Silva, M.J., Oliveira, E.D. (2009). Clues for detecting irony in user-generated contents: Oh...!! it’s “so easy” ;-). Proceedings of the 1st International CIKM Workshop on Topic-Sentiment Analysis for Mass Opinion Measurement. Hong Kong, China, 53–56.
  47. Chardon, B., Benamara, F., Mathieu, Y.Y., Popescu, V., Asher, N. (2013). Measuring the effect of discourse structure on sentiment analysis. 14th International Conference on Intelligent Text Processing and Computational Linguistics, Samos, Greece, 25–37.
  48. Charniak, E., Johnson, M. (2005). Coarse-to-fine n-best parsing and maxent discriminative reranking. Proceedings of the 43rd Annual Meeting of the Association for Computational Linguistics, Ann Arbor, MI, 173–180.
  49. Choi, Y., Cardie, C. (2008). Learning with compositional semantics as structural inference for subsentential sentiment analysis. Proceedings of the Conference on Empirical Methods in Natural Language Processing, Honolulu, HI, 793–801.
  50. Clark, H.H., Gerrig, R.J. (1984). On the pretense theory of irony. Journal of Experimental Psychology: General, 113(1), 121–126.
  51. Clift, R. (1999). Irony in conversation. Language in Society, 28, 523–553.
  52. Cohen, J. (1988). Statistical Power Analysis for the Behavior Science. Lawrence Erlbaum Associates, Mahwah, NJ.
  53. Colston, H.L., Keller, S.B. (1998). You’ll never believe this: irony and hyperbole in expressing surprise. Journal of Psycholinguistic Research, 27(4), 499–513.
  54. Darwish, K. (2013). Named entity recognition using cross-lingual resources: arabic as an example. Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics, Sofia, Bulgaria, 1558–1567.
  55. Davidov, D., Rappoport, A. (2006). Efficient unsupervised discovery of word categories using symmetric patterns and high frequency words. Proceedings of the 21st International Conference on Computational Linguistics and 44th Annual Meeting of the Association for Computational Linguistics. Sydney, Australia, 297–304.
  56. Davidov, D., Tsur, O., Rappoport, A. (2010). Semi-supervised recognition of sarcastic sentences in twitter and amazon. Proceedings of the 14th Conference on Computational Natural Language Learning, Uppsala, Sweden, 107–116.
  57. Davidson, R.J., Scherer, K.R., Goldsmith, H.H. (2009). In Handbook of Affective Sciences, Oxford University Press, Oxford.
  58. Didio, L. (2007). Une approche émantico-sémiotique de l’ironie. Doctorate Thesis, Université de Limoges, Limoges, France.
  59. Do Dinh, E.-L., Gurevych, I. (2016). Token-level metaphor detection using neural networks. Proceedings of the 4th Workshop on Metaphor in NLP, San Diego, CA, 28–33. Available at: http://www.aclweb.org/anthology/W16-1104
  60. Eskander, R., Habash, N., Rambow, O. (2013). Automatic extraction of morphological lexicons from morphologically annotated corpora. Proceedings of the Conference on Empirical Methods in Natural Language Processing, Seattle, WA, 1032–1043.
  61. Farghaly, A., Senellart, J. et al. (2003). Intuitive coding of the arabic lexicon. SYSTRAN, MT, Summit IX Workshop, Machine Translation for Semitic Languages: Issues and Approaches, New Orleans, USA.
  62. Farias, D.I.H., Sulis, E., Patti, V., Ruffo, G., Bosco, C. (2015). Valento: sentiment analysis of figurative language tweets with irony and sarcasm. SemEval-2015, Duisburg, Germany, 694–698.
  63. Filatova, E. (2012). Irony and sarcasm: corpus generation and analysis using crowdsourcing. In LREC, Calzolari, N., Choukri, K., Declerck, T., Dogan, M.U., Maegaard, B., Mariani, J., Odijk, J., Piperidis, S. (eds). European Language Resources Association, Istanbul, Turkey, 392–398.
  64. Fromilhague, C. (1995). Les figures de style. Armand Colin, Paris, France.
  65. Gedigian, M., Bryant, J., Narayanan, S., Ciric, B. (2006). Catching metaphors. Proceedings of the 3rd Workshop on Scalable Natural Language Understanding, New York, NY, 41–48.
  66. Ghosh, A., Li, G., Veale, T., Rosso, P., Shutova, E., Barnden, J., Reyes, A. (2015). Semeval-2015 task 11: sentiment analysis of figurative language in Twitter. Proceedings of Sem Eval 2015, Co-located with NAACL, ACL, Beijing, China, 470–478.
  67. Gianti, A., Bosco, C., Patti, V., Bolioli, A., Caro, L.D. (2012). Annotating irony in a novel italian corpus for sentiment analysis. Proceedings of the 4th Workshop on Corpora for Research on Emotion Sentiment and Social Signals, Istanbul, Turkey.
  68. Gibbs, R.W. (1994). The Poetics of Mind: Figurative Thought, Language, and Understanding. Cambridge University Press, Cambridge.
  69. Gibbs, R.W. (2000). Irony in talk among friends. Metaphor and Symbol, 15(1–2), 5–27.
  70. Gonzalez-Ibanez, R., Muresan, S., Wacholde, N. (2011). Identifying sarcasm in Twitter: a closer look. Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies: Short Papers-2, Portland, OR, 581–586.
  71. Goode, B.J., Reyes, J.I.M., Pardo-Yepez, D.R., Canale, G.L., Tong, R.M., Mares, D., Roan, M., Ramakrishnan, N. (2017). Time-series analysis of blog and metaphor dynamics for event detection. In Advances in Cross-Cultural Decision Making, Schatz, S., Hoffman, M. (eds). Springer, 17–27.
  72. Graja, M., Jaoua, M., Belguith, L.H. (2011). Building ontologies to understand spoken tunisian dialect. CoRR. Available at: http://arxiv.org/abs/1109.0624.
  73. Green, S., Manning, C.D. (2010). Better arabic parsing: baselines, evaluations, and analysis. Proceedings of the 23rd International Conference on Computational Linguistics. Beijing, China, 394–402.
  74. Grice, H.P. (1970). Logic and Conversation. Harvard University, Cambridge, MA.
  75. Grice, H.P., Cole, P., Morgan, J.L. (1975). Syntax and semantics. In Logic and Conversation, Grice, H.P. (ed.)., Harvard University, Cambridge, MA, 3, 41–58.
  76. Habash, N., Rambow, O. (2006). Magead: a morphological analyzer and generator for the arabic dialects. Proceedings of the 21st International Conference on Computational Linguistics and 44th Annual Meeting of the Association for Computational Linguistics, Sydney, Australia, 681–688.
  77. Habash, N., Rambow, O. (2007). Morphophonemic and orthographic rules in a multi-dialectal morphological analyzer and generator for arabic verbs. International Symposium on Computer and Arabic Language, Riyadh, Saudi Arabia.
  78. Habash, N.Y. (2010). Introduction to Arabic natural language processing. Morgan & Claypool, San Rafael, CA.
  79. Haiman, J. (1998). Talk is cheap: sarcasm, alienation, and the evolution of language. Oxford University Press, Oxford.
  80. Haiman, J. (2001). Talk is cheap: sarcasm, alienation, and the evolution of language. Oxford University Press, Oxford.
  81. Hammo, B., Abu-Salem, H., Lytinen, S. (2002). Qarab: a question answering system to support the Arabic language. Proceedings of the ACL-02 Workshop on Computational Approaches to Semitic Languages, Philadelphia, PA, 1–11.
  82. Hatzivassiloglou, V., McKeown, K.R. (1997). Predicting the semantic orientation of adjectives. Proceedings of the 8th Conference on European Chapter of the Association for Computational Linguistics, Madrid, Spain, 174–181.
  83. Hee, C.V., Lefever, E., Hoste, V. (2016). Exploring the realization of irony in Twitter data. In Proceedings of the 10th International Conference on Language Resources and Evaluation (LREC 2016). In Chair, N.C.C., Choukri, K., Declerck, T., Goggi, S., Grobelnik, M., Maegaard, B., Mariani, J., Mazo, H., Moreno, A., Odijk, J., Piperidis, S. (eds). European Language Resources Association, Paris, France.
  84. Hertzler, J.O. (1970). Laughter: a socio-scientific analysis. Exposition Press, New York, USA.
  85. Huang, T.-H.K. (2014). Social metaphor detection via topical analysis. 6th International Joint Conference on Natural Language Processing. Nagoya, Japan.
  86. Hunston, S., Thompson, G. (eds). (2000). Evaluation in Text: Authorial Distance and the Construction of Discourse. Oxford University Press, Oxford.
  87. Hyungsuk, J., Ploux, S., Wehrli, E. (2003). Lexical knowledge representation with contexonyms. 9th MT Summit Machine Translation, New Orleans, LA, 194–201.
  88. Jang, H., Moon, S., Jo, Y., Rosé, C.P. (2015). Metaphor detection in discourse. 16th Annual Meeting of the Special Interest Group on Discourse and Dialogue, Prague, Czech Republic.
  89. Jang, H., Wen, M., Rosé, C.P. (2015). Effects of situational factors on metaphor detection in an online discussion forum. Proceedings of the 3rd Workshop on Metaphor in NLP, Denver, CO, 1–10.
  90. Jie Tang, Y., Chen, H.-H. (2014). Chinese irony corpus construction and ironic structure analysis. Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: Technical Papers, Dublin, Ireland, 1269–1278.
  91. Joshi, A., Sharma, V., Bhattacharyya, P. (2015). Harnessing context incongruity for sarcasm detection. Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing, Beijing, China, 757–762.
  92. Joshi, A., Tripathi, V., Patel, K., Bhattacharyya, P., Carman, M. (2016). Are word embedding-based features useful for sarcasm detection? Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, Austin, TX, 1006–1011.
  93. Karoui, J. (2016). Fric: un corpus et un schéma d’annotation multiniveau pour l’ironie dans les tweets. Atelier Communautés: outils et applications en TAL, dans le cadre de la conférence JEP-TALN-RECITAL 2016, Paris, France.
  94. Karoui, J., Graja, M., Boudabous, M., Belguith, L.H. (2013). Domain ontology construction from a tunisian spoken dialogue corpus. Proc. ICWIT, Hammamet, Tunisia.
  95. Karoui, J., Benamara, F., Moriceau, V., Aussenac-Gilles, N., Belguith, L.H. (2015a). Towards a contextual pragmatic model to detect irony in tweets. Proceedings of ACL-IJCNLP 2015, Volume 2: Short Papers. The Association for Computer Linguistics, Beijing, China, 644–650. Available at: http://aclweb.org/anthology/P/P15/P15-2106.pdf.
  96. Karoui, J., Benamara Zitoune, F., Moriceau, V., Aussenac-Gilles, N., Hadrich Belguith, L. (2015b). Détection automatique de l’ironie dans les tweets en français. Actes de la 22e conférence sur le traitement automatique des langues naturelles, Caen, France, 460–465. Available at: http://www.atala.org/taln_archives/TALN/TALN-2015/taln-2015-court-022.
  97. Karoui, J., Benamara, F., Moriceau, V., Patti, V., Bosco, C., Aussenac-Gilles, N. (2017). Exploring the impact of pragmatic phenomena on irony detection in tweets: a multilingual corpus study. Proceedings of the 15th edition of the European Chapter of the Association for Computational Linguistics Conference, Valencia, Spain.
  98. Kerbrat-Orecchioni, C. (1976). Problèmes de l’ironie. Linguistique et sémiologie, 2, 10–46.
  99. Keskes, I., Zitoune, F.B., Belguith, L.H. (2014). Learning explicit and implicit Arabic discourse relations. Journal of King Saud University-Computer and Information Sciences, 26(4), 398–416.
  100. Kintsch, W. (2000). Metaphor comprehension: a computational theory. Psychonomic Bulletin & Review, 7(2), 257–266.
  101. Kittay, E.F. (1990). Metaphor: Its Cognitive Force and Linguistic Structure, Oxford University Press, Oxford.
  102. Kreuz, R.J. (1996). The use of verbal irony: cues and constraints. Metaphor: Implications and Applications, Part 1, Chapter 2, 23–38.
  103. Kreuz, R.J., Caucci, G.M. (2007). Lexical influences on the perception of sarcasm. Proceedings of the Workshop on Computational Approaches to Figurative Language. New York, NY, 1–4.
  104. Kreuz, R.J., Glucksberg, S. (1989). How to be sarcastic: the echoic reminder theory of verbal irony. Journal of Experimental Psychology: General, 118(4), 374.
  105. Kreuz, R.J., Roberts, R.M. (1993). The empirical study of figurative language in literature. Poetics, 22(1), 151–169.
  106. Kreuz, R.J., Roberts, R.M. (1995). Two cues for verbal irony: hyperbole and the ironic tone of voice. Metaphor and Symbol, 10(1), 21–31.
  107. Kumon-Nakamura, S., Glucksberg, S., Brown, M. (1995). How about another piece of pie: the allusional pretense theory of discourse irony. Journal of Experimental Psychology: General, 124(1), 3.
  108. Leech, G.N. (2016). Principles of Pragmatics. Routledge, London.
  109. Létourneau, D., Bélanger, M. (2009). Impacts de la variation du nombre de traits discriminants sur la catégorisation des documents. Actes du 5e Défi Fouille de Textes, Paris, France.
  110. Liebrecht, C., Kunneman, F., Van Den, B.A. (2013). The perfect solution for detecting sarcasm in tweets# not. Proceedings of the 4th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis, Atlanta, GA29–37.
  111. Littman, T., Turney, P. (2002). Unsupervised learning of semantic orientation from a hundred-billion-word corpus, Technical Report ERB-1094, National Research Council Canada, Canada.
  112. Liu, B. (2012). Sentiment analysis and opinion mining. Synthesis Lectures on Human Language Technologies, 5(1), 1–167.
  113. Liu, B. (2015). Sentiment Analysis: Mining Opinions, Sentiments, and Emotions. Cambridge University Press, Cambridge.
  114. Losada, D.E., Crestani, F. (2016). A test collection for research on depression and language use. Experimental IR Meets Multilinguality, Multimodality, and Interaction, 28–39. Available at: http://dx.doi.org/10.1007/978-3-319-44564-9_3.
  115. Lucariello, J. (1994). Situational irony: a concept of events gone awry. Journal of Experimental Psychology: General, 123(2), 129.
  116. Maamouri, M., Bies, A., Kulick, S. (2006). Diacritization: a challenge to Arabic tree bank annotation and parsing. Proceedings of the Conference of the Machine Translation SIG of the British Computer Society, London.
  117. Macwhinney, B., Fromm, D. (2014). Two approaches to metaphor detection. Proceedings of the 9th International Conference on Language Resources and Evaluation. In Chair, N.C.C., Choukri, K., Declerck, T., Loftsson, H., Maegaard, B., Mariani, J., Moreno, A., Odijkand, J., Piperidis, S. (eds). European Language Resources Association, Reykjavik, Iceland.
  118. Marton, Y., Habash, N., Rambow, O. (2013). Dependency parsing of modern standard Arabic with lexical and inflectional features. Computational Linguistics, 39(1), 161–194.
  119. Masmoudi, A., Khemakhem, M.E., Estève, Y., Bougares, F., Dabbar, S., Belguith, L.H. (2014). Phonétisation automatique du dialecte tunisien. 30e Journée d’études sur la parole, Le Mans, France.
  120. Maynard, D., Greenwood, M.A. (2014). Who cares about sarcastic tweets? Investigating the impact of sarcasm on sentiment analysis. LREC, 4238–4243.
  121. Mercier-Leca, F. (2003). L’ironie. Hachette, Paris.
  122. Mihalcea, R., Strapparava, C. (2005). Making computers laugh : investigations in automatic humor recognition. Proceedings of the Conference on Human Language Technology and Empirical Methods in Natural Language Processing, Vancouver, Canada, 531–538.
  123. Mihalcea, R., Strapparava, C. (2006). Learning to laugh (automatically): computational models for humor recognition. Computational Intelligence, 22(2), 126–142.
  124. Miller, G.A. (1995). Wordnet: a lexical database for english. Communications of the ACM, 38(11), 39–41.
  125. Mpouli, S., Ganascia, J.-G. (2015). Extraction et analyse automatique des comparaisons et des pseudo-comparaisons pour la détection des comparaisons figuratives. 22e Conférence sur le traitement automatique des langues naturelles, Caen, France.
  126. Muecke, D.C. (1978). Irony markers. Poetics, 7(4), 363–375.
  127. Nadaud, B., Zagaroli, K. (2008). Surmonter ses complexes: les comprendre pour les assumer. Eyrolles, Paris, France.
  128. Niogret, P. (2004). Les figures de l’ironie dans À la recherche du temps perdu de Marcel Proust. L’Harmattan, Paris, France.
  129. Oliveira, I., Ploux, S. (2009). Vers une méthode de détection et de traitement automatique de la métaphore. Passeurs de mots, passeurs d’espoir. Actes des 8e Journées scientifiques du Réseau LTT, Lisbon, Portugal, 1–11.
  130. Oudah, M., Shaalan, K.F. (2012). A pipeline Arabic named entity recognition using a hybrid approach. Proceedings of COLING 2012: Technical Papers, Mumbai, India, 2159–2176.
  131. Ounis, I., Macdonald, C., Soboroff, I. (2008). Overview of the trec-2008 blog track. Technical Report, Glasgow.
  132. Pang, B., Lee, L. (2004). A sentimental education: sentiment analysis using subjectivity summarization based on minimum cuts. Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics, Barcelona, Spain.
  133. Pang, B., Lee, L., Vaithyanathan, S. (2002). Thumbs up? Sentiment classification using machine learning techniques. Proceedings of the ACL-02 Conference on Empirical Methods in Natural Language Processing, 10, 79–86.
  134. Pasha, A., Al-Badrashiny, M., Diab, M.T., El Kholy, A., Eskander, R., Habash, N., Pooleery, M., Rambow, O., Roth, R. (2014). Madamira: a fast, comprehensive tool for morphological analysis and disambiguation of Arabic. LREC, 14, 1094–1101.
  135. Péry-Woodley, M.-P., Asher, N., Enjalbert, P., Benamara, F., Bras, M., Fabre, C., Ferrari, S., Ho-Dac, L.-M., Le Draoulec, A., Mathet, Y. et al. (2009). Annodis: une approche outillée de l’annotation de structures discursives. TALN 2009 Conférence sur le traitement automatique des langues naturelles, Paris, France.
  136. Polanyi, L., Zaenen, A. (2006). Contextual valence shifters. Computing Attitude and Affect in Text: Theory and Applications. Springer-Verlag, Berlin, 1–10.
  137. Politis, H. (2002). Kierkegaard. Ellipses, New York, USA.
  138. Pougeoise, M. (2001). Dictionnaire de rhétorique. Armand Colin, Paris, France.
  139. Purandare, A., Litman, D. (2006). Humor: prosody analysis and automatic recognition for friends. Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing, Sydney, Australia, 208–215.
  140. Radev, D., Stent, A., Tetreault, J., Pappu, A., Iliakopoulou, A., Chanfreau, A., de Juan, P., Vallmitjana, J., Jaimes, A., Jha, R. et al. (2015). Humor in collective discourse: unsupervised funniness detection in the New Yorker cartoon caption contest. Computation and Language, 1, 475–479.
  141. Raeber, T. (2011). L’ironie: réactualisation de pensée et contenus non posés: une approche pragmatique. Master’s Thesis, Université de Neuchâtel, Neuchâtel, Switzerland.
  142. Raz, Y. (2012). Automatic humor classification on Twitter. Proceedings of the 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Student Research Workshop, Montreal, Canada, 66–70.
  143. Reboul, O. (1991). Introduction à la rhétorique. Théorie et pratique. Presses universitaires de France, Paris.
  144. Reyes, A., Rosso, P. (2011). Mining subjective knowledge from customer reviews: a specific case of irony detection. Proceedings of the 2nd Workshop on Computational Approaches to Subjectivity and Sentiment Analysis, Portland, OR, 118–124.
  145. Reyes, A., Rosso, P. (2012). Making objective decisions from subjective data: detecting irony in customer reviews. Decision Support Systems, 53(4), 754–760.
  146. Reyes, A., Rosso, P. (2014). On the difficulty of automatically detecting irony: beyond a simple case of negation. Knowledge and Information Systems, 40(3), 595–614.
  147. Reyes, A., Rosso, P., Buscaldi, D. (2009). Humor in the blogosphere: first clues for a verbal humor taxonomy. Journal of Intelligent Systems, 18(4), 311–332.
  148. Reyes, A., Rosso, P., Veale, T. (2013). A multidimensional approach for detecting irony in Twitter. Language Resources and Evaluation, 47(1), 239–268.
  149. Rick, S., Loewenstein, G. (2008). The role of emotion in economic behavior. In Handbook of Emotions, Lewis, M., Haviland-Jones, J.M., Barrett, L.F. (eds). The Guilford Press, New York, 138–156.
  150. Riffaterre, M. (1969). La métaphore filée dans la poésie surréaliste. Langue française, 3(1), 46–60.
  151. Riloff, E., Qadir, A., Surve, P., Silva, L.D., Gilbert, N., Huang, R. (2013). Sarcasm as contrast between a positive sentiment and negative situation. EMNLP, Sofia, Bulgaria, 704–714.
  152. Ritchie, D. (2005). Frame-shifting in humor and irony. Metaphor and Symbol, 20(4), 275–294.
  153. Rouvier, M., Favre, B., Andiyakkal Rajendran, B. (2015). Talep deft’15: le plus cooool des systèmes d’analyse de sentiment. Actes du 11e Défi Fouille de Texte. Association pour le traitement automatique des langues, Caen, France, 97–103. Available at: http://www.atala.org/taln_archives/DEFT/DEFT-2015/deft-2015-long-011.
  154. Roze, C., Danlos, L., Muller, P. (2012). Lexconn: a French lexicon of discourse connectives. Discours. Revue de linguistique, psycholinguistique et informatique. A journal of linguistics, psycholinguistics and computational linguistics. Presses universitaires de Caen, France.
  155. Ryding, K.C. (2005). A reference grammar of modern standard Arabic. Cambridge University Press, Cambridge.
  156. Sadat, F., Mohamed, E. (2013). Improved Arabic-French machine translation through preprocessing schemes and language analysis. In Canadian Conference on Artificial Intelligence, Springer-Verlag, Berlin, 308–314.
  157. Saif, M., Mohammad, S., Svetlana, K. (2016). Sentiment lexicons for Arabic social media. Proceedings of the 10th International Conference on Language Resources and Evaluation LREC 2016. In Calzolari, N., Choukri, K., Declerck, T., Goggi, S., Grobelnik, M., Maegaard, B., Mariani, J., Mazo, H., Moreno, A., Odijk, J., Piperidis, S. (eds). Portorož, Slovenia. Available at: http://www.lrec-conf.org/proceedings/lrec2016/summaries/234.html.
  158. Searle, J. (1979). Expression and Meaning: Studies in the Theory of Speech Acts. Cambridge University Press, Cambridge.
  159. Seto, K.-I. (1998). On non-echoic irony. Relevance Theory: Applications and Implications, 37, 239.
  160. Shaikh, M.A., Prendinger, H., Mitsuru, I. (2007). Assessing sentiment of text by semantic dependency and contextual valence analysis. Proceedings of the International Conference on Affective Computing and Intelligent Interaction, Springer-Verlag, Berlin, Germany, 191–202.
  161. Shelley, C. (2001). The bicoherence theory of situational irony. Cognitive Science, 25(5), 775–818.
  162. Shutova, E., Devereux, B.J., Korhonen, A. (2013). Conceptual metaphor theory meets the data: a corpus-based human annotation study. Language resources and evaluation, 47(4), 1261–1284.
  163. Simédoh, V. (2012). L’humour et l’ironie en littérature francophone subsaharienne: des enjeux critiques à une poétique du rire. Peter Lang, Berlin, Germany.
  164. Sjöbergh, J., Araki, K. (2007). Recognizing humor without recognizing meaning. International Workshop on Fuzzy Logic and Applications, Springer-Verlag, Berlin, Germany, 469–476.
  165. Song, N.S. (1998). Metaphor and metonymy. Relevance Theory: Applications and Implications, John Benjamins Publishing, Amsterdam, The Netherlands, 37, 87–104.
  166. Sperber, D., Wilson, D. (1981). Irony and the use-mention distinction. Radical pragmatics, 49, 295–318.
  167. Stenetorp, P., Pyysalo, S., Topić, G., Ohta, T., Ananiadou, S., Tsujii, J. (2012). Brat: a web-based tool for nlp-assisted text annotation. Proceedings of the Demonstrations at the 13th Conference of the European Chapter of the Association for Computational Linguistics. Avignon, France, 102–107.
  168. Su, C., Huang, S., Chen, Y. (2017). Automatic detection and interpretation of nominal metaphor based on the theory of meaning. Neurocomputing, 219, 300–311.
  169. Sulis, E., Hernández Farías, D.I., Rosso, P., Patti, V., Ruffo, G. (2016). Figurative messages and affect in Twitter: differences between #irony, #sarcasm and #not. Knowledge-Based Systems. Available at: https://doi.org/10.1016/j.knosys.2016.05.035.
  170. Taboada, M., Brooke, J., Tofiloski, M., Voll, K., Stede, M. (2011). Lexicon-based methods for sentiment analysis. Computational Linguistics, 37, 267–307.
  171. Taylor, J.M. (2009). Computational detection of humor: a dream or a nightmare? The ontological semantics approach. Proceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology, Washington, DC, 429–432.
  172. Tayot, C. (1984). L’ironie. Doctorate Thesis, Université Claude Bernard, Lyon, France.
  173. Toprak, C., Gurevych, I. (2009). Document level subjectivity classification experiments in DEFT 2009 challenge. Actes du 5e Défi Fouille de Textes, Senlis, France.
  174. Tsur, O., Davidov, D., Rappoport, A. (2010). ICWSM a great catchy name: semi-supervised recognition of sarcastic sentences in online product reviews. ICWSM, George Washington University, Washington.
  175. Tsvetkov, Y., Boytsov, L., Gershman, A., Nyberg, E., Dyer, C. (2014). Metaphor detection with cross-lingual model transfer. Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, Baltimore, MD, 248–258.
  176. Turney, P.D. (2002). Thumbs up or thumbs down? Semantic orientation applied to unsupervised classification of reviews. Proceedings of the 40th Meeting of the Association for Computational Linguistics, Philadelphia, PA, 417–424.
  177. Utsumi, A. (1996). A unified theory of irony and its computational formalization. Proceedings of the 16th Conference on Computational Linguistics, Copenhague, Denmark, 962–967.
  178. Utsumi, A. (2000). Verbal irony as implicit display of ironic environment: distinguishing ironic utterances from non irony. Journal of Pragmatics, 32(12), 1777–1806.
  179. Utsumi, A. (2004). Stylistic and contextual effects in irony processing. Proceedings of the 26th Annual Meeting of the Cognitive Science Society. Lawrence Erlbaum Associates, Mahwah, New Jersey, 1369–1374.
  180. Van de Gejuchte, I. (1993). L’humour comme discours. Revue de l’Institut de sociologie, 12(1-4), 399–411.
  181. Van Hee, C., Lefever, E., Hoste, V. (2015). Guidelines for annotating irony in social media text. Technical Report, Department of Translation, Interpreting and Communication, Ghent University, Belgium.
  182. Van Hee, C., Lefever, E., Hoste, V. (2016). Exploring the realization of irony in Twitter data. Proceedings of the 10th International Conference on Language Resources and Evaluation, Portorož, Slovenia, 1795–1799.
  183. Veale, T., Hao, Y. (2010). Detecting ironic intent in creative comparisons. ECAI, 215, 765–770.
  184. Voas, D. (2014). Towards a sociology of attitudes. Sociological Research Online, 19(1), 12.
  185. Wallace, B.C. (2015). Computational irony: a survey and new perspectives. Artificial Intelleligence Review, 43(4), 467–483.
  186. Wallace, B.C., Choe, D.K., Kertz, L., Charniak, E. (2014). Humans require context to infer ironic intent (so computers probably do, too). ACL, (2), 512–516.
  187. Wallace, B.C., Choe, D.K., Charniak, E. (2015). Sparse, contextually informed models for irony detection: exploiting user communities, entities and sentiment. Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing of the Asian Federation of Natural Language Processing, Beijing, China, 1035–1044.
  188. Wen, M., Zheng, Z., Jang, H., Xiang, G., Rosé, C.P. (2013). Extracting events with informal temporal references in personal histories in online communities. ACL, (2), 836–842.
  189. Whissell, C. (1989). The dictionary of affect in language. Emotion: Theory, Research, and Experience, 4(113-131), 94.
  190. Wiebe, J., Wilson, T., Cardie, C. (2005). Annotating expressions of opinions and emotions in language. Language Resources and Evaluation, 39(2-3), 165–210.
  191. Wilks, Y. (1978). Making preferences more active. Artificial Intelligence, 11(3), 197–223.
  192. Wilson, D., Sperber, D. (1986). Relevance: Communication and Cognition, Harvard University Press, Cambridge.
  193. Wilson, D., Sperber, D. (1988). Representation and relevance. Mental Representations: The Interface Between Language and Reality, Cambridge University Press, Cambridge, 133–153.
  194. Wilson, D., Sperber, D. (1992). On verbal irony. Lingua, 87(1), 53–76.
  195. Wilson, D., Sperber, D. (2004). Relevance theory. In Handbook of Pragmatics. Blackwell Publishing, Oxford, 607–632.
  196. Yamanashi, M.-A. (1998). Some issues in the treatment of irony and related tropes. Relevance Theory: Applications and Implications, 37, 271.
  197. Yang, D., Lavie, A., Dyer, C., Hovy, E. (2015). Humor recognition and humor anchor extraction. Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, Lisbon, Portugal, 2367–2376.
  198. Zagibalov, T., Belyatskaya, K., Carroll, J. (2010). Comparable english-russian book review corpora for sentiment analysis. Proceedings of the 1st Workshop on Computational Approaches to Subjectivity and Sentiment Analysis, Lisbon, Portugal, 67–72.
..................Content has been hidden....................

You can't read the all page of ebook, please click here login for view all page.
Reset
18.219.219.197