A multiple criteria credit rating approach utilizing social media data

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dc.contributor.authorGul, Sait
dc.contributor.authorKabak, Ozgur
dc.contributor.authorTopcu, Ilker
dc.date.accessioned2024-03-13T10:34:55Z
dc.date.available2024-03-13T10:34:55Z
dc.date.issued2018
dc.departmentİstanbul Beykent Üniversitesien_US
dc.description.abstractCredit rating is a process for building a classification system for credit lenders to characterize current or potential credit borrowers. By such a process, financial institutions classify borrowers for lending decision by evaluating their financial and/or nonfinancial performances. Recently, use of social media data has emerged an important source of information. Accordingly, social media data can be very useful in evaluating companies' credibility when financial or non-financial assessments are missing or unreliable as well as when credit analyzers' subjective perceptions manipulate the decision. In this study, a multiple criteria credit rating approach is proposed to determine companies' credibility level utilizing social media data as well as financial measures. Additionally, to strengthen the lender's interpretation and inference competency, ratings are represented with a risk distribution based on cumulative belief degrees. Sentiment analysis, a web mining and text classification method, is used to collect social media data on Twitter. Importance of criteria is revealed through pairwise comparisons. Companies' performance scores and ratings are obtained by a cumulative belief degree approach. The proposed approach is applied to 64 companies. Results indicate that social media provides valuable information to determine companies' creditability. However credit ratings tend to decrease when social media data is considered.en_US
dc.description.sponsorshipIstanbul Technical University BAP [38818]; Scientific and Technological Research Council of Turkey (TUBITAK) [1649B031200042]en_US
dc.description.sponsorshipThis work was supported by Istanbul Technical University BAP [grant number 38818]. Additionally, Sait Gill has been beneficiary of The Scientific and Technological Research Council of Turkey (TUBITAK) scholarship programme (scholar id: 1649B031200042) since 2012.en_US
dc.identifier.doi10.1016/j.datak.2018.05.005
dc.identifier.endpage99en_US
dc.identifier.issn0169-023X
dc.identifier.issn1872-6933
dc.identifier.scopus2-s2.0-85047630908
dc.identifier.scopusqualityQ2en_US
dc.identifier.startpage80en_US
dc.identifier.urihttps://doi.org/10.1016/j.datak.2018.05.005
dc.identifier.urihttps://hdl.handle.net/20.500.12662/4135
dc.identifier.volume116en_US
dc.identifier.wosWOS:000441853500005
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoenen_US
dc.publisherElsevieren_US
dc.relation.ispartofData & Knowledge Engineeringen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectCredit ratingen_US
dc.subjectCumulative belief degreesen_US
dc.subjectSentiment analysisen_US
dc.subjectSocial mediaen_US
dc.subjectWeb miningen_US
dc.subjectText miningen_US
dc.titleA multiple criteria credit rating approach utilizing social media dataen_US
dc.typeArticleen_US

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