On the difficulty of automatically detecting irony: beyond a simple case of negation

The final publication is available at Springer via http://dx.doi.org/10.1007/s10115-013-0652-8It is well known that irony is one of the most subtle devices used to, in a refined way and without a negation marker, deny what is literally said. As such, its automatic detection would represent valuable knowledge regarding tasks as diverse as sentiment analysis, information extraction, or decision making. The research described in this article is focused on identifying key values of components to represent underlying characteristics of this linguistic phenomenon. In the absence of a negation marker, we focus on representing the core of irony by means of three conceptual layers. These layers involve 8 different textual features. By representing four available data sets with these features, we try to find hints about how to deal with this unexplored task from a computational point of view. Our findings are assessed by human annotators in two strata: isolated sentences and entire documents. The results show how complex and subjective the task of automatically detecting irony could be.The research work of Paolo Rosso was done in the framework of the European Commission WIQ-EI Web Information Quality Evaluation Initiative (IRSES grant no. 269180) project within the FP 7 Marie Curie People, the DIANA-APPLICATIONS - Finding Hidden Knowledge in Texts: Applications (TIN2012-38603-C02-01) project, and the VLC/CAMPUS Microcluster on Multimodal Interaction in Intelligent Systems.Reyes Pérez, A.; Rosso, P. (2014). On the difficulty of automatically detecting irony: beyond a simple case of negation. Knowledge and Information Systems. 40(3):595-614. https://doi.org/10.1007/s10115-013-0652-8S595614403Artstein R, Poesio M (2008) Inter-coder agreement for computational linguistics. Comput Linguistics 34(4):555–596Atserias J, Casas B, Comelles E, González M, Padró L, Padró M (2006) Freeling 1.3: syntactic and semantic services in an open-source nlp library. 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Détection automatique de l'ironie dans les tweets en français

International audienceCet article présente une méthode par apprentissage supervisé pour la détection de l'ironie dans les tweets en français. Un classifieur binaire utilise des traits de l'état de l'art dont les performances sont reconnues, ainsi que de nouveaux traits issus de notre étude de corpus. En particulier, nous nous sommes intéressés à la négation et aux oppositions explicites/implicites entre des expressions d'opinion ayant des polarités différentes. Les résultats obtenus sont encourageants

¡Qué maravilla! Multimodal Sarcasm Detection in Spanish : a Dataset and a Baseline

Peer reviewe

Making objective decisions from subjective data: Detecting irony in customers reviews

[EN] The research described in this work focuses on identifying key components for the task of irony detection. By means of analyzing a set of customer reviews, which are considered ironic both in social and mass media, we try to find hints about how to deal with this task from a computational point of view. Our objective is to gather a set of discriminating elements to represent irony, in particular, the kind of irony expressed in such reviews. To this end, we built a freely available data set with ironic reviews collected from Amazon. Such reviews were posted on the basis of an online viral effect; i.e. contents that trigger a chain reaction in people. The findings were assessed employing three classifiers. Initial results are largely positive, and provide valuable insights into the subjective issues of language facing tasks such as sentiment analysis, opinion mining and decision making. (C) 2012 Elsevier B.V. All rights reserved.The National Council for Science and Technology (CONACyT - Mexico) has funded the research of the first author. The European Commission as part of the WIQEI IRSES-Project (grant no. 269180) within the FP 7 Marie Curie People Framework has partially funded this work. This work was carried out in the framework of the MICINN Text-Enterprise (TIN2009-13391-C04-03) research project and the Microcluster VLC/Campus (International Campus of Excellence) on Multimodal Intelligent Systems.Reyes Pérez, A.; Rosso, P. (2012). Making objective decisions from subjective data: Detecting irony in customers reviews. Decision Support Systems. 53(4):754-760. https://doi.org/10.1016/j.dss.2012.05.027S75476053

Patterns of Ironic Metaphors in Lithuanian Politicized Discourse

[full article and abstract in English] The present article is an attempt to examine the metaphoric models of ironic assessment employed in politicized public discourse in Lithuania. The examination follows the implications of the Blending theory (Fauconnier & Turner 2002), and discusses the topicality of the dominant metaphoric patterns in online newspaper headlines and commentaries, as well as in a number of posters the political parties of Lithuania prepared for the electoral campaign. The database of 200 newspaper headlines, comments, and posters allowed to identify dominant references to political issues in terms of sport, miracles, family, business and crime. Furthermore, the analysis has shown that attention should be drawn to aspects of social cognition and culture as they appear to be an integral part of the blending structure and are crucial in successful transmission of both the intended message and the evaluative attitude. Metaphors in the mode of irony follow a double-scope conceptual integration network, as the final blend comprises not only the elements of the two input spaces of the employed metaphor but also the elements of our background knowledge