Araştırma Makalesi
Yıl 2025,
Cilt: 9 Sayı: 2, 321 - 330, 28.06.2025
Öz
This study investigates user feedback on mobile applications of Turkish airlines, focusing on the key factors contributing to user satisfaction and dissatisfaction. By utilizing advanced text classification techniques such as sentiment analysis and Latent Dirichlet Allocation (LDA), the research decodes customer reviews from the Google Play Store and Apple App Store. The analysis identifies prevalent themes in user feedback, including issues related to usability, app performance, and customer service responsiveness. The results reveal that app updates, functionality issues, and customer support are critical areas where airlines need improvement. This study provides actionable insights for Turkish airlines to optimize their mobile applications, ultimately enhancing customer satisfaction and loyalty.
Anahtar Kelimeler
Turkish airlines Mobile applications Sentiment analysis Customer satisfaction Text mining
Kaynakça
- Amadeus. (2020). The Future of Airline IT: How Digital Transformation is Shaping the Aviation Industry. Amadeus Insights.
- Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent dirichlet allocation. Journal of Machine Learning Research, 3, 993-1022.
- Chung, H., & Kwon, T. H. (2009). The impact of mobile services on customer satisfaction in the airline industry. Journal of Airline and Service Management, 15(2), 143-159.
- Davis, F. D. (1989). Technology acceptance model: TAM. Al-Suqri, MN, Al-Aufi, AS: Information Seeking Behavior and Technology Adoption, 205(219), 5.
- Eberendu, A. C. (2016). Unstructured Data: an overview of the data of Big Data. International Journal of Computer Trends and Technology, 38(1), 46-50.
- Güres, N., Çavuş, Ş. A., & Mutlu, T. (2018). Understanding the impact of digital services on customer satisfaction in the airline industry. Journal of Travel and Tourism Management, 34(3), 117-130.
- Hussain, F., Ahmed, S., & Zafar, M. (2021). The role of mobile airline apps in enhancing service quality: An analysis of user reviews. Journal of Airline Operations and Services, 36(4), 290-305.
- Hutto, C., & Gilbert, E. (2014, May). Vader: A parsimonious rule-based model for sentiment analysis of social media text. In Proceedings of the international AAAI conference on web and social media (Vol. 8, No. 1, pp. 216-225).
- Kim, D., Oh, S., & Jeong, J. (2021). Analyzing user feedback for mobile airline applications through sentiment analysis. Journal of Aviation Technology and Management, 45(1), 27-38.
- Koçak, B. B., & Atalık, Ö. (2019). Perceptual maps of Turkish airline services for different periods using supervised machine learning approach and multidimensional scaling. International Journal of Sustainable Aviation, 5(3), 205-229.
- Kocak, B. B., Polat, I., & Kocak, C. B. (2016). Determination of Twitter Users Sentiment Polarity Toward Airline Market in Turkey: a Case of Opinion Mining. PressAcademia Procedia, 2(1), 684-691.
- Lee, J. K., Hosanagar, K., & Tan, T. (2020). Impact of customer-generated reviews on digital services: Evidence from the airline industry. MIS Quarterly, 44(4), 987-1010.
- Mittal, V., & Agrawal, P. (2022). A text mining approach to identify satisfaction determinants in airline app reviews. Journal of Airline Customer Insights, 11(1), 45-60.
- Parasuraman, A., Zeithaml, V. A., & Berry, L. L. (1988). Servqual: A multiple-item scale for measuring consumer perc. Journal of retailing, 64(1), 12.
- Shankar, R., Sharma, A., & Mittal, V. (2022). Enhancing sentiment classification in airline app reviews: The role of machine learning algorithms. International Journal of Aviation Technology, 48(2), 75-91.
- Statista. (2023). Percentage of internet users accessing services via mobile devices in Turkey. Statista Digital Reports.
- Stavrianou, A., Andritsos, P., & Nicoloyannis, N. (2007). Overview and semantic issues of text mining. ACM Sigmod Record, 36(3), 23-34.
- Taraban, R., Koduru, L., LaCour, M., & Marshall, P. (2018). Finding a common ground in human and machine- based text processing.
- Turban, E., Outland, J., King, D., Lee, J. K., Liang, T. P., & Turban, D. (2015). Electronic Commerce 2015: A Managerial and Social Networks Perspective. Springer.
Yıl 2025,
Cilt: 9 Sayı: 2, 321 - 330, 28.06.2025
Öz
Kaynakça
- Amadeus. (2020). The Future of Airline IT: How Digital Transformation is Shaping the Aviation Industry. Amadeus Insights.
- Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent dirichlet allocation. Journal of Machine Learning Research, 3, 993-1022.
- Chung, H., & Kwon, T. H. (2009). The impact of mobile services on customer satisfaction in the airline industry. Journal of Airline and Service Management, 15(2), 143-159.
- Davis, F. D. (1989). Technology acceptance model: TAM. Al-Suqri, MN, Al-Aufi, AS: Information Seeking Behavior and Technology Adoption, 205(219), 5.
- Eberendu, A. C. (2016). Unstructured Data: an overview of the data of Big Data. International Journal of Computer Trends and Technology, 38(1), 46-50.
- Güres, N., Çavuş, Ş. A., & Mutlu, T. (2018). Understanding the impact of digital services on customer satisfaction in the airline industry. Journal of Travel and Tourism Management, 34(3), 117-130.
- Hussain, F., Ahmed, S., & Zafar, M. (2021). The role of mobile airline apps in enhancing service quality: An analysis of user reviews. Journal of Airline Operations and Services, 36(4), 290-305.
- Hutto, C., & Gilbert, E. (2014, May). Vader: A parsimonious rule-based model for sentiment analysis of social media text. In Proceedings of the international AAAI conference on web and social media (Vol. 8, No. 1, pp. 216-225).
- Kim, D., Oh, S., & Jeong, J. (2021). Analyzing user feedback for mobile airline applications through sentiment analysis. Journal of Aviation Technology and Management, 45(1), 27-38.
- Koçak, B. B., & Atalık, Ö. (2019). Perceptual maps of Turkish airline services for different periods using supervised machine learning approach and multidimensional scaling. International Journal of Sustainable Aviation, 5(3), 205-229.
- Kocak, B. B., Polat, I., & Kocak, C. B. (2016). Determination of Twitter Users Sentiment Polarity Toward Airline Market in Turkey: a Case of Opinion Mining. PressAcademia Procedia, 2(1), 684-691.
- Lee, J. K., Hosanagar, K., & Tan, T. (2020). Impact of customer-generated reviews on digital services: Evidence from the airline industry. MIS Quarterly, 44(4), 987-1010.
- Mittal, V., & Agrawal, P. (2022). A text mining approach to identify satisfaction determinants in airline app reviews. Journal of Airline Customer Insights, 11(1), 45-60.
- Parasuraman, A., Zeithaml, V. A., & Berry, L. L. (1988). Servqual: A multiple-item scale for measuring consumer perc. Journal of retailing, 64(1), 12.
- Shankar, R., Sharma, A., & Mittal, V. (2022). Enhancing sentiment classification in airline app reviews: The role of machine learning algorithms. International Journal of Aviation Technology, 48(2), 75-91.
- Statista. (2023). Percentage of internet users accessing services via mobile devices in Turkey. Statista Digital Reports.
- Stavrianou, A., Andritsos, P., & Nicoloyannis, N. (2007). Overview and semantic issues of text mining. ACM Sigmod Record, 36(3), 23-34.
- Taraban, R., Koduru, L., LaCour, M., & Marshall, P. (2018). Finding a common ground in human and machine- based text processing.
- Turban, E., Outland, J., King, D., Lee, J. K., Liang, T. P., & Turban, D. (2015). Electronic Commerce 2015: A Managerial and Social Networks Perspective. Springer.
Toplam 19 adet kaynakça vardır.
Ayrıntılar
| Birincil Dil | İngilizce |
|---|---|
| Konular | Makine Öğrenme (Diğer) |
| Bölüm | Araştırma Makaleleri |
| Yazarlar | |
| Yayımlanma Tarihi | 28 Haziran 2025 |
| Gönderilme Tarihi | 21 Eylül 2024 |
| Kabul Tarihi | 8 Mart 2025 |
| Yayımlandığı Sayı | Yıl 2025 Cilt: 9 Sayı: 2 |
Journal of Aviation - JAV |
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