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Supervised sentiment analysis in multilingual environments
dc.contributor.author | Vilares, David | |
dc.contributor.author | Alonso, Miguel A. | |
dc.contributor.author | Gómez-Rodríguez, Carlos | |
dc.date.accessioned | 2024-01-17T16:40:58Z | |
dc.date.available | 2024-01-17T16:40:58Z | |
dc.date.issued | 2017-05 | |
dc.identifier.citation | Vilares, D., Alonso, M.A. and Gómez-Rodríguez, C. (2017) ‘Supervised sentiment analysis in multilingual environments’, Information Processing & Management, 53(3), pp. 595–607. doi:10.1016/j.ipm.2017.01.004. | es_ES |
dc.identifier.issn | 0306-4573 | |
dc.identifier.issn | 1873-5371 | |
dc.identifier.uri | http://hdl.handle.net/2183/34957 | |
dc.description | © 2017. This manuscript version is made available under the CC-BY-NC-ND 4.0 license https://creativecommons.org/licenses/by-nc-nd/4.0/. This version of the article: Vilares, D., Alonso, M.A. and Gómez-Rodríguez, C. (2017) ‘Supervised sentiment analysis in multilingual environments’ has been accepted for publication in Information Processing & Management, 53(3), pp. 595–607. The Version of Record is available online at https://doi.org/10.1016/j.ipm.2017.01.004. | es_ES |
dc.description.abstract | [Abstract]: This article tackles the problem of performing multilingual polarity classification on Twitter, comparing three techniques: (1) a multilingual model trained on a multilingual dataset, obtained by fusing existing monolingual resources, that does not need any language recognition step, (2) a dual monolingual model with perfect language detection on monolingual texts and (3) a monolingual model that acts based on the decision provided by a language identification tool. The techniques were evaluated on monolingual, synthetic multilingual and code-switching corpora of English and Spanish tweets. In the latter case we introduce the first code-switching Twitter corpus with sentiment labels. The samples are labelled according to two well-known criteria used for this purpose: the SentiStrength scale and a trinary scale (positive, neutral and negative categories). The experimental results show the robustness of the multilingual approach (1) and also that it outperforms the monolingual models on some monolingual datasets. | es_ES |
dc.description.sponsorship | This research was supported by the Ministerio de Economía y Competitividad (FFI2014-51978-C2) and Xunta de Galicia (R2014/034). David Vilares is funded by the Ministerio de Educación, Cultura y Deporte (FPU13/01180). Carlos Gómez-Rodríguez is funded by an Oportunius program grant (Xunta de Galicia). | es_ES |
dc.description.sponsorship | Xunta de Galicia; R2014/034 | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | Elsevier | es_ES |
dc.relation | info:eu-repo/grantAgreement/MINECO/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/FFI2014-51978-C2-1-R/ES/TECNOLOGIAS DE LA LENGUA PARA ANALISIS DE OPINIONES EN REDES SOCIALES | es_ES |
dc.relation | info:eu-repo/grantAgreement/MECD/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/FPU13%2F01180/ES/ | es_ES |
dc.relation.isversionof | 10.1016/j.ipm.2017.01.004 | |
dc.relation.uri | https://doi.org/10.1016/j.ipm.2017.01.004 | es_ES |
dc.rights | Atribución-NoComercial-SinDerivadas 4.0 Internacional | es_ES |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/3.0/es/ | * |
dc.subject | Sentiment analysis | es_ES |
dc.subject | Multilingual | es_ES |
dc.subject | Code-Switching | es_ES |
dc.title | Supervised sentiment analysis in multilingual environments | es_ES |
dc.type | info:eu-repo/semantics/article | es_ES |
dc.rights.access | info:eu-repo/semantics/openAccess | es_ES |
UDC.journalTitle | Information Processing and Management | es_ES |
UDC.volume | 53 | es_ES |
UDC.issue | 3 | es_ES |
UDC.startPage | 595 | es_ES |
UDC.endPage | 607 | es_ES |
dc.identifier.doi | 10.1016/j.ipm.2017.01.004 |
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