Perceptual maps of Turkish airline services for different periods using supervised machine learning approach and multidimensional scaling

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dc.contributor.authorKocak, Bahri Baran
dc.contributor.authorAtalik, Ozlem
dc.date.accessioned2024-04-24T17:17:59Z
dc.date.available2024-04-24T17:17:59Z
dc.date.issued2019
dc.departmentDicle Üniversitesien_US
dc.description.abstractIn the airline market, it is crucial for airline industry to determine the experiences, expectations and perceptions of passengers in order to apply positioning strategies on brands. In this study, we used 15,864 Turkish tweets sent to the official airline Twitter pages based in Turkey between 1st June and 1st September 2017. Then, we applied aspect-based sentiment analysis (ABSA) with supervised machine learning approach to classify tweets into airline service categories and sentiment polarity. Lastly, multidimensional scaling (MDS) employed to build perceptual maps of airline services for different periods. This study aims to explore how tweets reflect airline service quality attributes in perceptual maps for selected periods in Turkey. Our analysis shows that the perceptual positions of services change per period, which means that Twitter users perceived each service differently in each period. In terms of the importance of airline service quality attributes website services, convenience of flight, and in-flight entertainment were the most critical disparities perceived by users compared to other attributes considering in the periods being examined.en_US
dc.identifier.doi10.1504/IJSA.2019.103503
dc.identifier.endpage229en_US
dc.identifier.issn2050-0467
dc.identifier.issn2050-0475
dc.identifier.issue3en_US
dc.identifier.startpage205en_US
dc.identifier.urihttps://doi.org/10.1504/IJSA.2019.103503
dc.identifier.urihttps://hdl.handle.net/11468/18520
dc.identifier.volume5en_US
dc.identifier.wosWOS:000496154300003
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.language.isoenen_US
dc.publisherInderscience Enterprises Ltden_US
dc.relation.ispartofInternational Journal of Sustainable Aviation
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectAirlineen_US
dc.subjectServicesen_US
dc.subjectTwitteren_US
dc.subjectText Classificationen_US
dc.subjectSupervised Learningen_US
dc.subjectMultidimensional Scalingen_US
dc.subjectPerceptual Mapen_US
dc.titlePerceptual maps of Turkish airline services for different periods using supervised machine learning approach and multidimensional scalingen_US
dc.titlePerceptual maps of Turkish airline services for different periods using supervised machine learning approach and multidimensional scaling
dc.typeArticleen_US

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