Applying basic features from sentiment analysis on automatic irony detection

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-19390-8_38People use social media to express their opinions. Often linguistic devices such as irony are used. From the sentiment analysis perspective such utterances represent a challenge being a polarity reversor (usually from positive to negative). This paper presents an approach to address irony detection from a machine learning perspective. Our model considers structural features as well as, for the first time, sentiment analysis features such as the overall sentiment of a tweet and a score of its polarity. The approach has been evaluated over a set classifiers such as: Naïve Bayes, Decision Tree, Maximum Entropy, Support Vector Machine, and for the first time in irony detection task: Multilayer Perceptron. The results obtained showed the ability of our model to distinguish between potentially ironic and non-ironic sentences.The National Council for Science and Technology (CONACyT Mexico) has funded the research work of the first author (Grant No.218109/313683, CVU-369616). The research work of third author was carried out inthe framework of WIQ-EI IRSES (Grant No. 269180) within the FP 7 Marie Curie, DIANA-APPLICATIONS (TIN2012-38603-C02-01) projects and the VLC/CAMPUS Microcluster on Multimodal Interaction in Intelligent Systems.Hernández Farías, I.; Benedí Ruiz, JM.; Rosso, P. (2015). Applying basic features from sentiment analysis on automatic irony detection. En Pattern Recognition and Image Analysis: 7th Iberian Conference, IbPRIA 2015, Santiago de Compostela, Spain, June 17-19, 2015, Proceedings. Springer International Publishing. 337-344. https://doi.org/10.1007/978-3-319-19390-8_38337344Alba-Juez, L.: Irony and the other off record strategies within politeness theory. J. Engl. Am. Stud. 16, 13–24 (1995)Attardo, S.: Irony markers and functions: towards a goal-oriented theory of irony and its processing. Rask 12, 3–20 (2000)Barbieri, F., Saggion, H.: Modelling Irony in Twitter, pp. 56–64. Association for Computational Linguistics (2014)Bosco, C., Patti, V., Bolioli, A.: Developing corpora for sentiment analysis: the case of irony and senti-tut. IEEE Intell. Syst. 28(2), 55–63 (2013)Buschmeier, K., Cimiano, P., Klinger, R.: 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, pp. 42–49. Association for Computational Linguistics (2014)Ghosh, A., Li, G., Veale, T., Rosso, P., Shutova, E., Reyes, A., Barnden, J.: Sentiment analysis of figurative language in twitter. In: Proceedings of the International Workshop on Semantic Evaluation (SemEval-2015), Co-located with NAACL and *SEM (2015)Hu, M., Liu, B.: Mining and summarizing customer reviews. In: Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2004, pp. 168–177(2004)Maynard, D., Greenwood, M.: Who cares about sarcastic tweets? investigating the impact of sarcasm on sentiment analysis. In: Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC-2014), European Language Resources Association (ELRA) (2014)Pedersen, T., Patwardhan, S., Michelizzi, J.: Wordnet::similarity: measuring the relatedness of concepts. In: Proceedings of the 9th National Conference on Artificial Intelligence, pp. 1024–1025. Association for Computational LinguisticsReyes, A., Rosso, P., Veale, T.: A multidimensional approach for detecting irony in twitter. Lang. Resour. Eval. 47(1), 239–268 (2013)Wallace, B.C.: Computational irony: a survey and new perspectives. Artif. Intell. Rev. 43, 467–483 (2013)Wang, A.P.: #irony or #sarcasm – a quantitative and qualitative study based on twitter. In: Proceedings of the PACLIC: the 27th Pacific Asia Conference on Language, Information, and Computation, pp. 349–356. Department of English, National Chengchi University (2013)Whissell, C.: Using the revised dictionary of affect in language to quantify the emotional undertones of samples of natural languages. Psychol. Rep. 2, 509–521 (2009)Wolf, A.: Emotional expression online: gender differences in emoticon use. CyberPsychology Behavior 3, 827–833 (2000