A Generative AI-Driven Analysis of Airline Passenger Feedback: Revealing What Matters Most

The airline industry, characterized by intense competition, relies heavily on customer satisfaction to assess strengths and weaknesses. Online passenger reviews provide a rich source of data, capturing customers’ opinions, expectations, and emotions. Analyzing this feedback helps airlines identify areas for improvement and understand what matters most to passengers. This study employs a zero-shot prompting approach using Google Gemini to interpret Turkish Airlines reviews from Trip Advisor in 2024, demonstrating the model’s effectiveness without domain-specific fine-tuning. The findings highlight factors influencing perceived service quality, performance, and value, illustrating the potential of generative AI in specialized customer sentiment analysis and its practical applications in the airline industry.

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Publication Details

Journal
Fırat Üniversitesi Mühendislik Bilimleri Dergisi
Published
2025-09-30
DOI
https://doi.org/10.35234/fumbd.1708787
Primary Topic
Forecasting Techniques and Applications
Type
article
Field-Weighted Citation Impact
0.00
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article

A Generative AI-Driven Analysis of Airline Passenger Feedback: Revealing What Matters Most

Hakan Koçak
Fırat Üniversitesi Mühendislik Bilimleri Dergisi
Forecasting Techniques and Applications
article

A Generative AI-Driven Analysis of Airline Passenger Feedback: Revealing What Matters Most

Hakan Koçak
article en

Abstract

The airline industry, characterized by intense competition, relies heavily on customer satisfaction to assess strengths and weaknesses. Online passenger reviews provide a rich source of data, capturing customers’ opinions, expectations, and emotions. Analyzing this feedback helps airlines identify areas for improvement and understand what matters most to passengers. This study employs a zero-shot prompting approach using Google Gemini to interpret Turkish Airlines reviews from Trip Advisor in 2024, demonstrating the model’s effectiveness without domain-specific fine-tuning. The findings highlight factors influencing perceived service quality, performance, and value, illustrating the potential of generative AI in specialized customer sentiment analysis and its practical applications in the airline industry.

Fırat Üniversitesi Mühendislik Bilimleri DergisiVol. 37(2)
General Directorate of Highways (TR)
Openalex Percentile: Top 68%
Forecasting Techniques and Applications
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