Simple record

dc.contributor.authorEzpeleta, Enaitz
dc.contributor.authorZurutuza, Urko
dc.contributor.otherGómez Hidalgo, José María
dc.date.accessioned2019-04-08T11:11:10Z
dc.date.available2019-04-08T11:11:10Z
dc.date.issued2016
dc.identifier.isbn978-3-319-45509-9 Printen
dc.identifier.isbn978-3-319-45510-5 Onlineen
dc.identifier.otherhttps://katalogoa.mondragon.edu/janium-bin/janium_login_opac.pl?find&ficha_no=124601en
dc.identifier.urihttps://hdl.handle.net/20.500.11984/1183
dc.description.abstractIn the same way that short instant messages are more and more used, spam and non-legitimate campaigns through this type of communication systems are growing up. Those campaigns, besides being an illegal online activity, are a direct threat to the privacy of the users. Previous short messages spam filtering techniques focus on automatic text classification and do not take message polarity into account. Focusing on phone SMS messages, this work demonstrates that it is possible to improve spam filtering in short message services using sentiment analysis techniques. Using a publicly available labelled (spam/legitimate) SMS dataset, we calculate the polarity of each message and aggregate the polarity score to the original dataset, creating new datasets. We compare the results of the best classifiers and filters over the different datasets (with and without polarity) in order to demonstrate the influence of the polarity. Experiments show that polarity score improves the SMS spam classification, on the one hand, reaching to a 98.91% of accuracy. And on the other hand, obtaining a result of 0 false positives with 98.67% of accuracy.en
dc.description.sponsorshipGobierno Vascoes
dc.language.isoengen
dc.publisherSpringer International Publishingen
dc.rights© Springer International Publishing Switzerland 2016en
dc.subjectSMSen
dc.subjectspamen
dc.subjectpolarityen
dc.subjectsentiment analysisen
dc.subjectsecurityen
dc.titleShort Messages Spam Filtering Using Sentiment Analysisen
dcterms.accessRightshttp://purl.org/coar/access_right/c_abf2en
dcterms.sourceText, Speech, and Dialogue: 19th International Conference, TSD 2016, Brno , Czech Republic, September 12-16, 2016, Proceedingsen
local.contributor.groupAnálisis de datos y ciberseguridades
local.description.peerreviewedtrueen
local.identifier.doihttp://dx.doi.org/10.1007/978-3-319-45510-5_17en
local.relation.projectIDGV/Proyectos de Investigación Básica y Aplicada 2014-2016/PC2014-08 PI_2014_1_102 /CAPV/Seguimiento y filtrado de spam personalizado en medios sociales mediante modelos de difusión y análisis del contenido/SOCIALSPAMen
local.source.detailsVol. 9924. Lecture Notes in Computer Science. Pp 142-153, 2016eu_ES
oaire.format.mimetypeapplication/pdf
oaire.file$DSPACE\assetstore
oaire.resourceTypehttp://purl.org/coar/resource_type/c_3248en
oaire.versionhttp://purl.org/coar/version/c_ab4af688f83e57aaen


Files in this item

Thumbnail

This item appears in the following Collection(s)

Simple record