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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.relation.projectID | info:eu-repo/grantAgreement/MICINN//TIN2009-13391-C04-03/ES/Text-Enterprise 2.0: Tecnicas De Comprension De Textos Aplicadas A Las Necesidades De La Empresa 2.0/ | |
dc.relation.projectID | info:eu-repo/grantAgreement/EC/FP7/grant no. 269180/EU/ | |
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. https://doi.org/10.1007/s10579-012-9196-x | es_ES |
dc.description.accrualMethod | S | 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 | |
dc.contributor.funder | European Commission | |
dc.contributor.funder | Ministerio de Ciencia e Innovación | |
dc.contributor.funder | Consejo Nacional de Ciencia y Tecnología, México | |
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