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dc.contributor.authorHenríquez Miranda, Carlosspa
dc.contributor.authorBriceño Díaz, Freddyspa
dc.contributor.authorSalcedo, Dixonspa
dc.date.accessioned2019-11-13T20:13:45Z
dc.date.available2019-11-13T20:13:45Z
dc.date.issued2019-08-12
dc.identifier.urihttp://hdl.handle.net/11323/5648spa
dc.description.abstractThis paper presents an unsupervised model for Aspect-Based Sentiment Analysis in Spanish language, which automatically extracts the aspects of opinion and determines its associated polarity. The model uses ontologies for the detection of explicit and implicit aspects, and machine learning without supervision to determine the polarity of a grammatical structure in Spanish. The unsupervised approach used, allows implementing a system quickly scalable to any language or domain. The experimental work was carried out using the dataset used in Semeval 2016 for Task 5 corresponding to Sentence-level ABSA. The implemented system obtained a 73.07 F1 value in the extraction of aspects and 84.8% accuracy in the sentiment classification. The system obtained the best results of all systems participating in the competition in the three issues mentioned above.spa
dc.language.isoeng
dc.publisherUniversidad de la Costaspa
dc.rightsCC0 1.0 Universalspa
dc.rights.urihttp://creativecommons.org/publicdomain/zero/1.0/spa
dc.subjectAspect-basedspa
dc.subjectOntologyspa
dc.subjectSentiment analysisspa
dc.subjectUnsupervised machine learningspa
dc.titleUnsupervised model for aspect-based sentiment analysis in spanishspa
dc.typePre-Publicaciónspa
dc.source.urlhttp://www.iaeng.org/IJCS/issues_v46/issue_3/IJCS_46_3_06.pdfspa
dc.rights.accessrightsinfo:eu-repo/semantics/openAccessspa
dc.identifier.instnameCorporación Universidad de la Costaspa
dc.identifier.reponameREDICUC - Repositorio CUCspa
dc.identifier.repourlhttps://repositorio.cuc.edu.co/spa
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dc.title.translatedModelo no supervisado para análisis de sentimiento basado en aspectos in españolspa
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