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Applying basic features from sentiment analysis on automatic irony detection

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Applying basic features from sentiment analysis on automatic irony detection

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dc.contributor.author Hernández Farías, Irazú es_ES
dc.contributor.author Benedí Ruiz, José Miguel es_ES
dc.contributor.author Rosso, Paolo es_ES
dc.date.accessioned 2016-05-17T12:09:41Z
dc.date.available 2016-05-17T12:09:41Z
dc.date.issued 2015-06-09
dc.identifier.isbn 978-3-319-19389-2
dc.identifier.issn 0302-9743
dc.identifier.uri http://hdl.handle.net/10251/64255
dc.description The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-19390-8_38 es_ES
dc.description.abstract 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. es_ES
dc.description.sponsorship 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. es_ES
dc.language Inglés es_ES
dc.publisher Springer International Publishing es_ES
dc.relation.ispartof Pattern Recognition and Image Analysis: 7th Iberian Conference, IbPRIA 2015, Santiago de Compostela, Spain, June 17-19, 2015, Proceedings es_ES
dc.relation.ispartofseries Lecture Notes in Computer Science;9117
dc.rights Reserva de todos los derechos es_ES
dc.subject Automatic irony detection es_ES
dc.subject Figurative language processing es_ES
dc.subject Sentiment analysis es_ES
dc.subject.classification LENGUAJES Y SISTEMAS INFORMATICOS es_ES
dc.title Applying basic features from sentiment analysis on automatic irony detection es_ES
dc.type Capítulo de libro es_ES
dc.identifier.doi 10.1007/978-3-319-19390-8_38
dc.relation.projectID info:eu-repo/grantAgreement/CONACyT//218109%2F313683/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/EC/FP7/269180/EU/Web Information Quality Evaluation Initiative/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/CONACyT//CVU-369616/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/MINECO//TIN2012-38603-C02-01/ES/DIANA-APPLICATIONS: FINDING HIDDEN KNOWLEDGE IN TEXTS: APPLICATIONS/ es_ES
dc.rights.accessRights Abierto 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 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 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion http://link.springer.com/chapter/10.1007/978-3-319-19390-8_38 es_ES
dc.description.upvformatpinicio 337 es_ES
dc.description.upvformatpfin 344 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.relation.senia 302891 es_ES
dc.contributor.funder Consejo Nacional de Ciencia y Tecnología, México es_ES
dc.contributor.funder European Commission es_ES
dc.contributor.funder Universitat de València es_ES
dc.contributor.funder Ministerio de Economía y Competitividad es_ES
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