Classification of Customer Demands by Using Doc2Vec Feaure Extraction Method

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dc.authoridARSLAN, Halil/0000-0003-3286-5159
dc.contributor.authorArslan, Halil
dc.contributor.authorKaynar, Oguz
dc.contributor.authorSahin, Sumeyye
dc.date.accessioned2024-10-26T17:59:53Z
dc.date.available2024-10-26T17:59:53Z
dc.date.issued2019
dc.departmentSivas Cumhuriyet Üniversitesi
dc.description27th Signal Processing and Communications Applications Conference (SIU) -- APR 24-26, 2019 -- Sivas Cumhuriyet Univ, Sivas, TURKEY
dc.description.abstractCompanies provide after-service communication with their customers through help desk systems they provide in call centers and websites. In companies with large networks, the amount of data collected by using these communication tools is increasing day by day.The separation of these collected requests and feedbacks to be transferred to the relevant units has become a very time consuming process. The prolongation of this process in customer-oriented companies can lead to customer loss.Therefore, it has importance to transfer, evaluate and return requests from such firms. In this study, firstly, two different models were used to obtain the features from the demands of customers. The features obtained were classified by multi-layer artificial neural network and it was provided the related demand was transferred to the related unit. Thus, the usability of the Doc2Vec model, which can be used as an alternative to the classic word bag, is examined in Turkish text classification studies.
dc.description.sponsorshipIEEE Turkey Sect,Turkcell,Turkhavacilik Uzaysanayii,Turitak Bilgem,Gebze Teknik Univ,SAP, Detaysoft,NETAS,Havelsan
dc.identifier.doi10.1109/siu.2019.8806452
dc.identifier.isbn978-1-7281-1904-5
dc.identifier.issn2165-0608
dc.identifier.scopus2-s2.0-85071966261
dc.identifier.scopusqualityN/A
dc.identifier.urihttps://doi.org/10.1109/siu.2019.8806452
dc.identifier.urihttps://hdl.handle.net/20.500.12418/27392
dc.identifier.wosWOS:000518994300129
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isotr
dc.publisherIEEE
dc.relation.ispartof2019 27th Signal Processing and Communications Applications Conference (Siu)
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectCustomer demands
dc.subjectDoc2Vec
dc.subjectClassification
dc.titleClassification of Customer Demands by Using Doc2Vec Feaure Extraction Method
dc.typeConference Object

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