Abstract
With the advances in information and communication technologies, an immense amount of information has been shared on social media and microblogging platforms. Much of the online content contains elements of figurative language, such as, irony, sarcasm and satire. The automatic identification of figurative language can be viewed as a challenging task in natural language processing, where linguistic entities, such as, metaphor, analogy, ambiguity, irony, sarcasm, satire, and so on, have been utilized to express more complex meanings. The predictive performance of sentiment classification schemes may degrade if figurative language within the text has not been properly addressed. Satirical text is a way of figurative communication, where ideas/opinions regarding a people, event or issue is expressed in a humorous way to criticize that entity. Satirical news can be deceptive and harmful. In this paper, we present a machine learning based approach to satire detection in Turkish news articles. In the presented scheme, we utilized three kinds of features to model lexical information, namely, unigrams, bigrams and tri-grams. In addition, term-frequency, term-presence and TF-IDF based schemes have been taken into consideration. In the classification phase, Naïve Bayes, support vector machines, logistic regression and C4.5 algorithms have been examined.
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References
Ramsey, R.: Affect and political satire: how political TV satire implicates internal political efficacy and political participation. University of the Pacific, MA Thesis (2018)
Fersini, E., Messina, E., Pozzi, F.A.: Sentiment analysis: Bayesian ensemble learning. Decis. Support Syst. 68, 26–38 (2014)
Onan, A.: Topic-enriched word embeddings for sarcasm identification. In: Silhavy, R. (ed.) CSOC 2019. AISC, vol. 984, pp. 293–304. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-19807-7_29
Cambria, E.: Affective computing and sentiment analysis. IEEE Intell. Syst. 31(2), 102–107 (2016)
Poria, S., Cambria, E., Hazarika, D., Vij, P.: A deeper look into sarcastic tweets using deep convolutional neural networks. In: Proceedings of COLING 2016, pp. 1601–1612. ACM, New York (2016)
Davidov, D., Tsur, O., Rappoport, A.: Semi-supervised recognition of sarcastic sentences in Twitter and Amazon. In: Proceedings of the Fourteenth Conference on Computational Natural Language Learning, pp. 107–116. ACM, New York (2010)
Gonzalez-Ibanez, R., Muresan, S., Wacholder, N.: Identifying sarcasm in Twitter: a closer look. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pp. 581–586. ACM, New York (2011)
Filatova, E.: Irony and sarcasm: corpus generation and analysis using crowdsourcing. In: Proceedings of Language Resources and Evaluation Conference, pp. 392–398. ACM, New York (2012)
Salas-Zarate, M., Paredes-Valverde, M.A., Rodriguez-Garcia, M.A., Valencia-Garica, R., Alor-Hernandez, G.: Automatic detection of satire in Twitter: a psycholinguistic-based approach. Knowl.-Based Syst. 128, 20–33 (2017)
Ahmad, T., Akhtar, H., Chopra, A., Akhtar, M.W.: Satire detection from web documents using machine learning methods. In: Proceedings of International Conference on Soft Computing and Machine Intelligence, pp. 102–105. IEEE, New York (2014)
Barbieri, F., Ronzano, F., Saggion, H.: Is this tweet satirical? a computational approach for satire detection in Spanish. Procesamiento del Lenguaje Nat. 55, 135–142 (2015)
Barbieri, F., Ronzano, F., Saggion, H.: Do we criticise (and laugh) in the same way? automatic detection of multi-lingual satirical news in Twitter. In: Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence, pp. 1215–1221. AAAI Press, New York (2015)
Rubin, V., Conroy, N., Chen, Y., Cornwell, S.: Fake news or truth? using satirical cues to detect potentially misleading news. In: Proceedings of the Second Workshop on Computational Approaches to Deception Detection, pp. 7–17. ACL, New York (2016)
Delmonte, R., Stingo, M.: Detecting satire in italian political commentaries. In: Nguyen, N.-T., Manolopoulos, Y., Iliadis, L., Trawiński, B. (eds.) ICCCI 2016. LNCS (LNAI), vol. 9876, pp. 68–77. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-45246-3_7
Perez-Rosas, V., Kleinberg, B., Lefevre, A., Mihalcea, R.: Automatic detection of fake news. arXiv preprint arXiv:1708.07104 (2017)
Ahmed, H., Traore, I., Saad, S.: Detection of online fake news using n-gram analysis and machine learning techniques. In: Traore, I., Woungang, I., Awad, A. (eds.) ISDDC 2017. LNCS, vol. 10618, pp. 127–138. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-69155-8_9
Yang, F., Mukherjee, A., Dragut, E.: Satirical news detection and analysis using attention mechanism and linguistic features. arXiv preprint arXiv:1709.01189 (2017)
Ravi, K., Ravi, V.: Irony detection using neural network language model, psycholinguistic features and text mining. In: Proceedings of IEEE 17th International Conference on Cognitive Informatics and Cognitive Computing, pp. 254–260. IEEE, New York (2018)
Onan, A.: Classifier and feature set ensembles for web page classification. J. Inf. Sci. 42(2), 150–165 (2016)
Cortes, C., Vapnik, V.: Support-vector networks. Mach. Learn. 20(3), 273–297 (1995)
Kantardzic, M.: Data Mining: Concepts, Models, Methods and Algorithms. Wiley, Hoboken (2011)
Gehrke, J.: The Handbook of Data Mining. Lawrence Erlbaum Associates, Chicago (2003)
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Toçoğlu, M.A., Onan, A. (2019). Satire Detection in Turkish News Articles: A Machine Learning Approach. In: Younas, M., Awan, I., Benbernou, S. (eds) Big Data Innovations and Applications. Innovate-Data 2019. Communications in Computer and Information Science, vol 1054. Springer, Cham. https://doi.org/10.1007/978-3-030-27355-2_8
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