Automatic Detection of Irony and Humour in Twitter

Aus de_evolutionary_art_org
Wechseln zu: Navigation, Suche


Francesco Barbieri and Horacio Saggion: Automatic Detection of Irony and Humour in Twitter. In: Computational Creativity 2014 ICCC 2014, 155-162.



Irony and humour are just two of many forms of figurative language. Approaches to identify in vast volumes of data such as the internet humorous or ironic statements is impor- tant not only from a theoretical view point but also for their potential applicability in social networks or human-computer interactive systems. In this study we investigate the auto- matic detection of irony and humour in social networks such as Twitter casting it as a classification problem. We propose a rich set of features for text interpretation and representation to train classification procedures. In cross-domain classification experiments our model achieves and improves state-of-the-art performance.

Extended Abstract


author = {Francesco Barbieri and Horacio Saggion},
title = {Automatic Detection of Irony and Humour in Twitter},
booktitle = {Proceedings of the Fifth International Conference on Computational Creativity},
series = {ICCC2014},
year = {2014},
month = {Jun},
location = {Ljubljana, Slovenia},
pages = {155-162},
url = {, },
publisher = {International Association for Computational Creativity},
keywords = {computational, creativity},

Used References

Barbieri, F., and Saggion, H. 2014. Modelling Irony in Twitter. In Proceedings of the Student Research Workshop at the 14th Conference of the European Chapter of the Asso- ciation for Computational Linguistics, 56–64. Gothenburg, Sweden: Association for Computational Linguistics. Bontcheva, K.; Derczynski, L.; Funk, A.; Greenwood,

M. A.; Maynard, D.; and Aswani, N. 2013. Twitie: An open-source information extraction pipeline for microblog text. In Proceedings of the International Conference on Re- cent Advances in Natural Language Processing. Association for Computational Linguistics.

Carvalho, P.; Sarmento, L.; Silva, M. J.; and de Oliveira, E. 2009. Clues for detecting irony in user-generated contents: oh...!! it’s so easy;-). In Proceedings of the 1st international CIKM workshop on Topic-sentiment analysis for mass opin- ion, 53–56. ACM.

Davidov, D.; Tsur, O.; and Rappoport, A. 2010. Semi- supervised recognition of sarcastic sentences in twitter and amazon. In Proceedings of the Fourteenth Conference on Computational Natural Language Learning, 107–116. As- sociation for Computational Linguistics.

Esuli, A., and Sebastiani, F. 2006. Sentiwordnet: A publicly available lexical resource for opinion mining. In Proceed- ings of Language Resources and Evaluation Conference, volume 6, 417–422.

Giora, R. 1995. On irony and negation. Discourse processes 19(2):239–264.

Grice, H. P. 1975. Logic and conversation. 1975 41–58. Howe, D. C. 2009. Rita wordnet. java based api to access wordnet.

Ide, N., and Suderman, K. 2004. The American National Corpus First Release. In Proceedings of the Language Re- sources and Evaluation Conference.

Lucariello, J. 2007. Situational irony: A concept of events gone away. Irony in language and thought 467–498.

Mihalcea, R., and Pulman, S. G. 2007. Characterizing hu- mour: An exploration of features in humorous texts. In CI- CLing, 337–347.

Mihalcea, R., and Strapparava, C. 2005. Making computers laugh: Investigations in automatic humor recognition. In HLT/EMNLP.

Miller, G. A. 1995. Wordnet: a lexical database for english. Communications of the ACM 38(11):39–41.

Pang, B., and Lee, L. 2008. Opinion Mining and Sentiment Analysis. Found. Trends Inf. Retr. 2(1-2):1–135.

Potts, C. 2011. Developing adjective scales from user- supplied textual metadata. NSF Workshop on Restructuring Adjectives in WordNet. Arlington,VA.

Quintilien, and Butler, H. E. 1953. The Institutio Oratoria of Quintilian. With an English Translation by HE Butler. W. Heinemann.

Reyes, A.; Rosso, P.; and Veale, T. 2013. A multidimen- sional approach for detecting irony in twitter. Language Re- sources and Evaluation 1–30.

Ritchie, G., and Masthoff, J. 2011. The STANDUP 2 in- teractive riddle builder. In Ventura, D.; Gerv ́as, P.; Harrell, D. F.; Maher, M. L.; Pease, A.; and Wiggins, G., eds., Pro- ceedings of the Second International Conference on Compu- tational Creativity, 159.

Ritchie, G. 2003. The jape riddle generator: technical spec- ification. Technical report, University of Edingurgh.

Spell, B. 2009. Java api for wordnet searching (jaws).

Stock, O., and Strapparava, C. 2006. Laughing with ha- hacronym, a computational humor system. In AAAI, 1675– 1678.

Taylor, J., and Mazlack, L. 2005. Toward computational recognition of humorous intent. In Cognitive Science Con- ference.

Utsumi, A. 2000. Verbal irony as implicit display of ironic environment: Distinguishing ironic utterances from non- irony. Journal of Pragmatics 32(12):1777–1806.

Veale, T., and Hao, Y. 2010a. Detecting ironic intent in creative comparisons. In ECAI, volume 215, 765–770.

Veale, T., and Hao, Y. 2010b. An ironic fist in a vel- vet glove: Creative mis-representation in the construction of ironic similes. Minds and Machines 20(4):635–650.

Veale, T. 2004. The challenge of creative information re- trieval. In Computational Linguistics and Intelligent Text Processing. Springer. 457–467.

Veale, T. 2013. Humorous similes. Humor 26(1):3–22. Venour, C. 2013. A computational model of lexical incon- gruity in humorous text. Ph.D. Dissertation, University of Aberdeen.

Wilson, D., and Sperber, D. 2002. Relevance theory. Hand- book of pragmatics.

Witten, I. H., and Frank, E. 2005. Data Mining: Practical machine learning tools and techniques. Morgan Kaufmann.


Full Text

intern file

Sonstige Links