Examining Consumer Reviews of Airline Mobile Applications Using Topic Modelling and Sentiment Analysis

The in-depth analysis of customer feedback in improving service quality forms the basis of modern marketing strategies. This study aims to examine user experiences with airline mobile applications using Text Mining methods based on the Service Dominant Logic (S-DL) framework, with reference to the SERVQUAL model dimensions. Within this scope, 22,296 user reviews of airline applications selected from the Google Play Store were analyzed in the Python environment. Latent Dirichlet Allocation (LDA) topic modeling was used to identify dominant themes, and the DistilRoBERTa-base algorithm was used to detect emotional states. The analysis results show that users' digital experiences cluster around the topics of User Experience, App Performance, App Updates, Flight and Booking Experience, Login Problems and General Issues, Digital Service Satisfaction, and Ticketing and Reservation Process. The findings reveal that technical issues, particularly login problems and software updates, disrupt the value-creation process. The findings of the research provide actionable strategic insights for airlines to improve service quality in mobile applications, which are operational resources, and to prevent value destruction.