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dc.contributor.authorEzpeleta, Enaitz
dc.contributor.authorGaritano, Iñaki
dc.contributor.authorZurutuza, Urko
dc.contributor.otherGómez Hidalgo, José María
dc.date.accessioned2019-04-04T13:45:44Z
dc.date.available2019-04-04T13:45:44Z
dc.date.issued2017
dc.identifier.issn0218-4885 Printen
dc.identifier.issn1793-6411 Onlineen
dc.identifier.otherhttps://katalogoa.mondragon.edu/janium-bin/janium_login_opac.pl?find&ficha_no=128257en
dc.identifier.urihttps://hdl.handle.net/20.500.11984/1181
dc.description.abstractCurrently, short communication channels are growing up due to the huge increase in the number of smartphones and online social networks users. This growth attracts malicious campaigns, such as spam campaigns, that are a direct threat to the security and privacy of the users. While most researches are focused on automatic text classification, in this work we demonstrate the possibility of improving current short messages spam detection systems using a novel method. We combine personality recognition and sentiment analysis techniques to analyze Short Message Services (SMS) texts. We enrich a publicly available dataset adding these features, first separately and after in combination, of each message to the dataset, creating new datasets. We apply several combinations of the best SMS spam classifiers and filters to each dataset in order to compare the results of each one. Taking into account the experimental results we analyze the real inuence of each feature and the combination of both. At the end, the best results are improved in terms of accuracy, reaching to a 99.01% and the number of false positive is reduced.en
dc.description.sponsorshipGobierno Vascoes
dc.language.isoengen
dc.publisherWorld Scientific Publishingen
dc.rights© World Scientific Publishing Company. Electronic version of an article published as International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems. Vol. 25. Nº. Suppl. 2. December, 2017 https://www.worldscientific.com/worldscinet/ijufksen
dc.subjectspamen
dc.subjectpolarityen
dc.subjectSMSen
dc.subjectsentiment analysisen
dc.subjectsecurityen
dc.titleShort Messages Spam Filtering Combining Personality Recognition and Sentiment Analysisen
dcterms.accessRightshttp://purl.org/coar/access_right/c_abf2en
dcterms.sourceInternational Journal of Uncertainty, Fuzziness and Knowledge-Based Systemsen
local.contributor.groupAnálisis de datos y ciberseguridades
local.description.peerreviewedtrueen
local.identifier.doihttps://doi.org/10.1142/S0218488517400177en
local.relation.projectIDGV/Proyectos de Investigación Básica y Aplicada 2014-2016/PC2014-08/CAPV/Seguimiento y filtrado de spam personalizado en medios sociales mediante modelos de difusión y análisis del contenido/SOCIALSPAMen
local.source.detailsVol. 25. Nº. Suppl. 2. December, 2017eu_ES
oaire.format.mimetypeapplication/pdf
oaire.file$DSPACE\assetstore
oaire.resourceTypehttp://purl.org/coar/resource_type/c_6501en
oaire.versionhttp://purl.org/coar/version/c_ab4af688f83e57aaen


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