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A multidimensional approach for detecting irony in Twitter

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A multidimensional approach for detecting irony in Twitter

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dc.contributor.author Reyes Pérez, Antonio es_ES
dc.contributor.author Rosso ., Paolo es_ES
dc.contributor.author Veale, Tony es_ES
dc.date.accessioned 2014-09-24T18:21:00Z
dc.date.issued 2013-03
dc.identifier.issn 1574-020X
dc.identifier.uri http://hdl.handle.net/10251/40166
dc.description.abstract Irony is a pervasive aspect of many online texts, one made all the more difficult by the absence of face-to-face contact and vocal intonation. As our media increasingly become more social, the problem of irony detection will become even more pressing. We describe here a set of textual features for recognizing irony at a linguistic level, especially in short texts created via social media such as Twitter postings or ¿tweets¿. Our experiments concern four freely available data sets that were retrieved from Twitter using content words (e.g. ¿Toyota¿) and user-generated tags (e.g. ¿#irony¿). We construct a new model of irony detection that is assessed along two dimensions: representativeness and relevance. Initial results are largely positive, and provide valuable insights into the figurative issues facing tasks such as sentiment analysis, assessment of online reputations, or decision making. es_ES
dc.description.sponsorship This work has been done in the framework of the VLC/CAMPUS Microcluster on Multimodal Interaction in Intelligent Systems and it has been partially funded by the European Commission as part of the WIQEI IRSES project (grant no. 269180) within the FP 7 Marie Curie People Framework, and by MICINN as part of the Text-Enterprise 2.0 project (TIN2009-13391-C04-03) within the Plan I+D+I. The National Council for Science and Technology (CONACyT - Mexico) has funded the research work of Antonio Reyes. en_EN
dc.language Inglés es_ES
dc.publisher Springer Netherlands es_ES
dc.relation.ispartof Language Resources and Evaluation es_ES
dc.rights Reserva de todos los derechos es_ES
dc.subject Irony detection es_ES
dc.subject Figurative language processing es_ES
dc.subject Negation es_ES
dc.subject Web text analysis es_ES
dc.subject.classification LENGUAJES Y SISTEMAS INFORMATICOS es_ES
dc.title A multidimensional approach for detecting irony in Twitter es_ES
dc.type Artículo es_ES
dc.embargo.lift 10000-01-01
dc.embargo.terms forever es_ES
dc.identifier.doi 10.1007/s10579-012-9196-x
dc.rights.accessRights Cerrado es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Sistemas Informáticos y Computación - Departament de Sistemes Informàtics i Computació es_ES
dc.description.bibliographicCitation Reyes Pérez, A.; Rosso ., P.; Veale, T. (2013). A multidimensional approach for detecting irony in Twitter. Language Resources and Evaluation. 47(1):239-268. doi:10.1007/s10579-012-9196-x es_ES
dc.description.accrualMethod Senia es_ES
dc.relation.publisherversion http://dx.doi.org/10.1007/s10579-012-9196-x es_ES
dc.description.upvformatpinicio 239 es_ES
dc.description.upvformatpfin 268 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 47 es_ES
dc.description.issue 1 es_ES
dc.relation.senia 255769
dc.identifier.eissn 1574-0218


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