Analyzing airport service quality through sentiment analysis using machine learning techniques
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Tarih
2024
Yazarlar
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Dergi ISSN
Cilt Başlığı
Yayıncı
CRC Press
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
The 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.
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Kaynak
Smart and Sustainable Operations Management in the Aviation Industry: A Supply Chain 4.0 Perspective
