Decoding Airline Passenger Sentiment Dynamics Using Transformer‐Based Models and Time‐Series Clustering

Bibliographic Data

ID21653644
AuthorsCarmen Kar Hang Lee (0000-0001-8878-939X, School of Business Singapore University of Social Sciences Singapore, corresponding author), Yu‐Chung Tsao (0000-0001-5058-8728, Department of Industrial Management National Taiwan University of Science and Technology Taipei Taiwan)
Year2025
Volume27
Issue2
Publication date2025-03-01
Open AccessYes
TypeARTICLE
VenueInternational Journal of Tourism Research (JOURNAL)
Journal identifiersISSN: 1099-2340 • E-ISSN: 1522-1970
PublisherWiley (PUBLISHER • GB)
DOI10.1002/jtr.2794
OpenAlexW4409370646
LanguageEN
References cited59

This study proposes a novel framework, namely the Sentiment Trend Analysis on Reviews (STAR). The STAR framework incorporates time‐series clustering into sentiment detection to discover distinct airline passenger sentiment patterns, followed by transformer‐based analysis to identify customer concerns from review text that drives negative sentiments. By applying the STAR framework to U.S. airlines, we identified notable differences in sentiment trends between low‐cost carriers (LCCs) and full‐service carriers (FSCs). Passengers are consistently dissatisfied with LCCs' baggage handling procedures and fees throughout the year and are more concerned about bookings and refunds during the Christmas holidays. For FSC passengers, flight cancellations and delays rank among their top concerns even during periods without major holidays. This study provides guidance for researchers and airlines to respond to the dynamics of customers' expectations and design recovery actions

Cluster analysis · Decoding methods · Econometrics · Economics · Geography · Telecommunications · Tourism · Transformer · Advanced Text Analysis Techniques · Anomaly Detection Techniques and Applications · Computer Science · Engineering · Time Series Analysis and Forecasting · Artificial Intelligence

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