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dc.contributor.authorParedes-Valverde, Mario Andrés
dc.contributor.authorColomo-Palacios, Ricardo
dc.contributor.authorSalas-Zárate, María del Pilar
dc.contributor.authorValencia-García, Rafael
dc.date.accessioned2018-09-05T11:53:28Z
dc.date.available2018-09-05T11:53:28Z
dc.date.created2017-10-27T09:47:07Z
dc.date.issued2017
dc.identifier.citationScientific Programming. 2017, 2017 1-6.nb_NO
dc.identifier.issn1058-9244
dc.identifier.urihttp://hdl.handle.net/11250/2560974
dc.description.abstractSentiment analysis is an important area that allows knowing public opinion of the users about several aspects. This information helps organizations to know customer satisfaction. Social networks such as Twitter are important information channels because information in real time can be obtained and processed from them. In this sense, we propose a deep-learning-based approach that allows companies and organizations to detect opportunities for improving the quality of their products or services through sentiment analysis. This approach is based on convolutional neural network (CNN) and word2vec. To determine the effectiveness of this approach for classifying tweets, we conducted experiments with different sizes of a Twitter corpus composed of 100000 tweets. We obtained encouraging results with a precision of 88.7%, a recall of 88.7%, and an -measure of 88.7% considering the complete dataset.nb_NO
dc.language.isoengnb_NO
dc.titleSentiment Analysis in Spanish for Improvement of Products and Services: A Deep Learning Approachnb_NO
dc.typeJournal articlenb_NO
dc.typePeer reviewednb_NO
dc.description.versionpublishedVersionnb_NO
dc.subject.nsiVDP::Teknologi: 500::Informasjons- og kommunikasjonsteknologi: 550nb_NO
dc.source.pagenumber1-6nb_NO
dc.source.volume2017nb_NO
dc.source.journalScientific Programmingnb_NO
dc.identifier.doi10.1155/2017/1329281
dc.identifier.cristin1508233
cristin.unitcode224,55,0,0
cristin.unitnameAvdeling for informasjonsteknologi
cristin.ispublishedtrue
cristin.fulltextoriginal
cristin.qualitycode1


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