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Applying basic features from sentiment analysis on automatic irony detection
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Abstract:
The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-19390-8_38 ; People 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_38 ; S ; 337 ; 344 ; Alba-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 Linguistics ; Reyes, 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)
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Keyword:
Automatic irony detection; Figurative language processing; LENGUAJES Y SISTEMAS INFORMATICOS; Sentiment analysis
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URL: https://doi.org/10.1007/978-3-319-19390-8_38
http://hdl.handle.net/10251/64255
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Overview of the Evalita 2014 SENTIment POLarity Classification Task
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In: Proceedings of the 4th evaluation campaign of Natural Language Processing and Speech tools for Italian (EVALITA'14) ; https://hal.inria.fr/hal-01228925 ; Proceedings of the 4th evaluation campaign of Natural Language Processing and Speech tools for Italian (EVALITA'14), 2014, Pisa, Italy. ⟨10.12871/clicit201429⟩ (2014)
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Overview of the Evalita 2014 SENTIment POLarity Classification Task
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In: Proceedings of the 4th evaluation campaign of Natural Language Processing and Speech tools for Italian (EVALITA'14) ; https://hal.inria.fr/hal-01195786 ; Proceedings of the 4th evaluation campaign of Natural Language Processing and Speech tools for Italian (EVALITA'14), Dec 2014, Pisa, France. ⟨10.12871/clicit201429⟩ (2014)
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