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Journal of Aviation
Article . 2025 . Peer-reviewed
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Decoding Customer Sentiments in Turkish Airlines Mobile Apps: A Comprehensive Text Mining Approach

Authors: Yavuz Selim Balcıoğlu;

Decoding Customer Sentiments in Turkish Airlines Mobile Apps: A Comprehensive Text Mining Approach

Abstract

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.

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Keywords

Makine Öğrenme (Diğer), Turkish airlines;Mobile applications;Sentiment analysis;Customer satisfaction;Text mining, Machine Learning (Other)

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
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