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dc.contributor.authorRoman, I.
dc.contributor.authorSantana, R.
dc.contributor.authorMendiburu, A.
dc.contributor.authorLozano, J.A.
dc.date.accessioned2019-10-08T20:14:00Z
dc.date.available2019-10-08T20:14:00Z
dc.date.issued2019
dc.identifier.isbn978-1-4503-6111-8
dc.identifier.urihttp://hdl.handle.net/20.500.11824/1017
dc.description.abstractSentiment analysis consists of evaluating opinions or statements based on text analysis. Among the methods used to estimate the degree to which a text expresses a certain sentiment are those based on Gaussian Processes. However, traditional Gaussian Processes methods use a prede- fined kernels with hyperparameters that can be tuned but whose structure can not be adapted. In this paper, we propose the application of Genetic Programming for the evolution of Gaussian Process kernels that are more precise for sentiment analysis. We use use a very flexible representation of kernels combined with a multi-objective approach that considers si- multaneously two quality metrics and the computational time required to evaluate those kernels. Our results show that the algorithm can outper- form Gaussian Processes with traditional kernels for some of the sentiment analysis tasks considered.en_US
dc.formatapplication/pdfen_US
dc.language.isoengen_US
dc.rightsReconocimiento-NoComercial-CompartirIgual 3.0 Españaen_US
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/3.0/es/en_US
dc.titleSentiment analysis with genetically evolved Gaussian kernelsen_US
dc.typeinfo:eu-repo/semantics/conferenceObjecten_US
dc.identifier.doi10.1145/3321707.3321779
dc.relation.publisherversionhttps://dl.acm.org/citation.cfm?doid=3321707.3321779en_US
dc.relation.projectIDES/1PE/SEV-2017-0718en_US
dc.relation.projectIDES/1PE/TIN2017-82626-Ren_US
dc.relation.projectIDEUS/BERC/BERC.2018-2021en_US
dc.relation.projectIDEUS/ELKARTEKen_US
dc.rights.accessRightsinfo:eu-repo/semantics/openAccessen_US
dc.type.hasVersioninfo:eu-repo/semantics/publishedVersionen_US
dc.journal.titleGECCO '19 Proceedings of the Genetic and Evolutionary Computation Conferenceen_US


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Reconocimiento-NoComercial-CompartirIgual 3.0 España
Except where otherwise noted, this item's license is described as Reconocimiento-NoComercial-CompartirIgual 3.0 España