Abstract
With the development of social networks and e-commerce, these media became regular spaces for ironic or sarcastic opinions. The detection of ironic opinions can help companies and government to improve products and services. Reliably identifying sarcasm and irony in text can improve the performance of natural language processing techniques applied to opinion mining, sentiment analysis and summarization. There are two main ways to detect irony in texts: features based classification and text classification without features. Most researchers focus their studies on the features creation that characterizes irony. However, there are new approaches that classify irony directly without feature creation. In this paper, we propose a new approach to detect irony by applying character language model classifiers without any feature engineering. We evaluated some algorithms from API LingPipe on Twitter and Amazon datasets including the SemEval-2018 Task 3 dataset for irony detection of English tweets. Several experiments were developed for analyzing the performance of each algorithm per each balanced and unbalanced collections created from the original datasets. The proposal obtained competitive values of accuracy, precision, recall and F1-measure.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
English Oxford Living Dictionaries: Irony, https://en.oxforddictionaries.com/definition/irony, last accessed 2018/04/28.
- 2.
English Oxford Living Dictionaries: Sarcasm, https://en.oxforddictionaries.com/definition/sarcasm, last accessed 2018/04/28.
- 3.
- 4.
Alias-i: LingPipe API Tutorials, http://alias-i.com/lingpipe/demos/tutorial/read-me.html, last accessed 2018/04/28 (2011).
- 5.
- 6.
- 7.
References
Attardo, S.: Irony as relevant inappropriateness. In: Irony in Language and Thought, pp. 135–174. Taylor and Francis Group (2007)
Buschmeier, K., Cimiano, P., Klinger, R.: An impact analysis of features in a classification approach to irony detection in product reviews. In: 5th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis, pp. 42–49. Association for Computational Linguistics, Baltimore (2014)
Colston, H.: On necessary conditions for verbal irony comprehension, pp. 97–134. Taylor and Francis Group (2007)
Filatova, E.: Irony and sarcasm: corpus generation and analysis using crowdsourcing. In: The International Conference on Language Resources and Evaluation LREC. European Language Resources Association (ELRA), New York (2012)
Grey, E.: Can’t take a Joke? That’s just Poe’s law, 2017’s most important internet phenomenon (2017)
Van Hee, C., Lefever, E., Hoste, V.: SemEval-2018 task 3: irony detection in English tweets. In: 12th International Workshop on Semantic Evaluation (SemEval 2018), New Orleans, LA, USA (2018)
Farías, H., Irazu, D.: Irony and sarcasm detection in Twitter: the role of affective content. Doctoral thesis. Universitat Politècnica de València (2017)
Huang, H.-H., Chen, C.-C., Chen, H.-H.: Disambiguating false-alarm hashtag usages in tweets for irony detection. In: 55th Annual Meeting of the Association for Computational Linguistics (ACL), Vancouver, Canada (2017)
Huang, Y.-H., Huang, H.-H., Chen, H.-H.: Irony detection with attentive recurrent neural networks. In: Jose, J.M., et al. (eds.) ECIR 2017. LNCS, vol. 10193, pp. 534–540. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-56608-5_45
Joshi, A., Bhattacharyya, P., Carman, M.J.: Automatic sarcasm detection: a survey. ACM Comput. Surv. 50, 5 (2016)
Kumon-Nakamura, S., Glucksberg, S., Brown, M.: How about another piece of pie: the allusional pretense theory of discourse irony. In: Irony in Language and Thought, pp. 57–96. Taylor and Francis Group (2007)
Ling, J., Klinger, R.: An empirical, quantitative analysis of the differences between sarcasm and irony. In: Semantic Sentiment and Emotion Workshop ESWC, Crete, Greece (2016)
Mohammad, S., Bravo-Marquez, F.: Emotion intensities in tweets. In: Proceedings of the 6th Joint Conference on Lexical and Computational Semantics, *SEM@ACM 2017, pp. 65–77 (2017)
Nozza, D., Fersini, E., Messina, E.: Unsupervised irony detection: a probabilistic model with word embeddings. In: 8th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2016), pp. 68–76 (2016)
Poe, N.: Big contradictions in the evolution theory (2005)
Reyes, A., Rosso, P., Buscaldi, D.: From humor recognition to irony detection: the figurative language of social media. Data Knowl. Eng. 74, 1–12 (2012)
Reyes, A., Rosso, P., Veale, T.: A multidimensional approach for detecting irony in Twitter. Lang. Resour. Eval. 47, 239–268 (2013)
Sotolongo, A., Arco, L., Rodríguez, A.: Detección de ironía en textos cortos enfocada a la minería de opinión. In: IV Conferencia Internacional en Ciencias Computacionales e Informáticas (CICCI’ 2018), XVII Convención y Feria Internacional Informática 2018, La Habana (2018)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Switzerland AG
About this paper
Cite this paper
Clavel Quintero, Y., Arco García, L. (2018). Irony Detection Based on Character Language Model Classifiers. In: Hernández Heredia, Y., Milián Núñez, V., Ruiz Shulcloper, J. (eds) Progress in Artificial Intelligence and Pattern Recognition. IWAIPR 2018. Lecture Notes in Computer Science(), vol 11047. Springer, Cham. https://doi.org/10.1007/978-3-030-01132-1_18
Download citation
DOI: https://doi.org/10.1007/978-3-030-01132-1_18
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-01131-4
Online ISBN: 978-3-030-01132-1
eBook Packages: Computer ScienceComputer Science (R0)