Analyzing airport service quality through sentiment analysis using machine learning techniques

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dc.contributor.authorAylak, Batin Latif
dc.date.accessioned2025-02-20T08:46:30Z
dc.date.available2025-02-20T08:46:30Z
dc.date.issued2024
dc.departmentTürk-Alman Üniversitesien_US
dc.description.abstractThe chapter highlights various machine learning models that have been employed to evaluate the effectiveness of airport services and raise customer satisfaction. The characteristics that influence traveller satisfaction and raise the standard of airport services have been identified using statistical techniques such as logistic regression, decision trees, and random forest models. Numerous studies have used decision trees to identify the factors that are most important in determining the standard of airport services and to provide recommendations for improvement based on the identified factors. Recurrent neural networks with long-term learning capabilities include those with long short-term memory. © 2025 selection and editorial matter, Turan Paksoy and Sercan Demir. All rights reserved.
dc.identifier.doi10.1201/9781003389187-6
dc.identifier.endpage91en_US
dc.identifier.isbn978-104010936-6
dc.identifier.isbn978-103248154-8
dc.identifier.scopus2-s2.0-85202817484
dc.identifier.startpage77en_US
dc.identifier.urihttps://doi.org/10.1201/9781003389187-6
dc.identifier.urihttps://hdl.handle.net/20.500.12846/1760
dc.indekslendigikaynakScopus
dc.institutionauthorAylak, Batin Latif
dc.language.isoen
dc.publisherCRC Press
dc.relation.ispartofSmart and Sustainable Operations Management in the Aviation Industry: A Supply Chain 4.0 Perspective
dc.relation.publicationcategoryKitap Bölümü - Uluslararası
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_Scopus_20250220
dc.titleAnalyzing airport service quality through sentiment analysis using machine learning techniques
dc.typeBook Part

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