E-Mail Classification Using Natural Language Processing

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dc.authoridsel, ilhami/0000-0003-0222-7017
dc.authoridHanbay, Davut/0000-0003-2271-7865
dc.authorwosidsel, ilhami/ABD-7350-2020
dc.authorwosidHanbay, Davut/AAG-8511-2019
dc.contributor.authorSel, Ilhami
dc.contributor.authorHanbay, Davut
dc.date.accessioned2024-08-04T20:46:46Z
dc.date.available2024-08-04T20:46:46Z
dc.date.issued2019
dc.departmentİnönü Üniversitesien_US
dc.description27th Signal Processing and Communications Applications Conference (SIU) -- APR 24-26, 2019 -- Sivas Cumhuriyet Univ, Sivas, TURKEYen_US
dc.description.abstractThanks to the rapid increase in technology and electronic communications, e-mail has become a serious communication tool. In many applications such as business correspondence, reminders, academic notices, web page memberships, e-mail is used as primary way of communication. If we ignore spam e-mails, there remain hundreds of e-mails received every day. In order to determine the importance of received e-mails, the subject or content of each e-mail must be checked. In this study we proposed an unsupervised system to classify received e-mails. Received e-mails' coordinates are determined by a method of natural language processing called as Word2Vec algorithm. According to the similarities, processed data are grouped by k-means algorithm with an unsupervised training model. In this study, 10517 e-mails were used in training. The success of the system is tested on a test group of 200 e-mails. In the test phase M3 model (window size 3, min. Word frequency 10, Gram skip) consolidated the highest success (91%). Obtained results are evaluated in section VI.en_US
dc.description.sponsorshipIEEE Turkey Sect,Turkcell,Turkhavacilik Uzaysanayii,Turitak Bilgem,Gebze Teknik Univ,SAP, Detaysoft,NETAS,Havelsanen_US
dc.identifier.doi10.1109/siu.2019.8806593
dc.identifier.isbn978-1-7281-1904-5
dc.identifier.issn2165-0608
dc.identifier.scopus2-s2.0-85071993162en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.urihttps://doi.org/10.1109/siu.2019.8806593
dc.identifier.urihttps://hdl.handle.net/11616/98948
dc.identifier.wosWOS:000518994300229en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isotren_US
dc.publisherIeeeen_US
dc.relation.ispartof2019 27th Signal Processing and Communications Applications Conference (Siu)en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectNatural Language Processingen_US
dc.subjectText Classificationen_US
dc.subjectWord2Vecen_US
dc.subjectSkip Gramen_US
dc.subjectK-meansen_US
dc.titleE-Mail Classification Using Natural Language Processingen_US
dc.typeConference Objecten_US

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