Please use this identifier to cite or link to this item: https://hdl.handle.net/10953/2292
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dc.contributor.authorJiménez Zafra, Salud María-
dc.contributor.authorMartín Valdivia, M. Teresa-
dc.contributor.authorMartínez Cámara, Eugenio-
dc.contributor.authorUreña López, L. Alfonso-
dc.date.accessioned2024-02-09T00:21:47Z-
dc.date.available2024-02-09T00:21:47Z-
dc.date.issued2019-01-
dc.identifier.citationS. M. Jimenez Zafra, M. T. Martin Valdivia, E. Martinez Camara and L. A. Urena Lopez, "Studying the Scope of Negation for Spanish Sentiment Analysis on Twitter," in IEEE Transactions on Affective Computing, vol. 10, no. 1, pp. 129-141, 1 Jan.-March 2019es_ES
dc.identifier.issn1949-3045es_ES
dc.identifier.otherhttps://doi.org/10.1109/TAFFC.2017.2693968es_ES
dc.identifier.urihttps://hdl.handle.net/10953/2292-
dc.description.abstractPolarity classification is a well-known Sentiment Analysis task. However, most research has been oriented towards developing supervised or unsupervised systems without paying much attention to certain linguistic phenomena such as negation. In this paper we focus on this specific issue in order to demonstrate that dealing with negation can improve the final system. Although we can find some studies of negation detection, most of them deal with English documents. On the contrary, our study is focused on the scope of negation in Spanish Sentiment Analysis. Thus, we have built an unsupervised polarity classification system based on integrating external knowledge. In order to evaluate the influence of negation we have implemented a specific module for negation detection by applying several rules. The system has been tested considering and without considering negation, using a corpus of tweets written in Spanish. The results obtained reveal that the treatment of negation can greatly improve the accuracy of the final system. Moreover, we have carried out a comprehensive statistical study in order to demonstrate our approach. To the best of our knowledge, this is the first work which statistically demonstrates that taking into account negation significantly improves the polarity classification of Spanish tweets.es_ES
dc.language.isoenges_ES
dc.publisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INCes_ES
dc.relation.ispartofIEEE Transactions on Affective Computinges_ES
dc.rightsAtribución-NoComercial-CompartirIgual 3.0 España*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/3.0/es/*
dc.subjectNegation scopees_ES
dc.subjectSentiment Analysises_ES
dc.subjectPolarity classificationes_ES
dc.subjectLexicon based systemes_ES
dc.titleStudying the Scope of Negation for Spanish Sentiment Analysis on Twitteres_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses_ES
dc.type.versioninfo:eu-repo/semantics/acceptedVersiones_ES
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