###### Abstract  
This study leverages approximately 7500 reviews from Skytrax to explore the determinants of airport service quality and their influence on passenger recommendations. The dataset includes various features such as terminal cleanliness, terminal seating, terminal signs, food and beverages, airport shopping, WiFi connectivity, and airport staff. The research employs a comprehensive methodology encompassing statistical data analysis, predictive modelling, and interaction effects analysis. The descriptive analysis of time-series data highlighted trends and fluctuations in service quality and recommendations, providing insights into temporal dynamics. Multiple machine learning models, including logistic regression, Random Forest, SVM, KNN, Gradient Boosting, and Neural Networks, were developed in this study and cross-validated for airport recommendation based on Skytrax's online reviews. Among others, Gradient Boosting emerged as the most accurate model with an 88.15% mean accuracy. Interaction effects revealed significant combined influences, such as terminal cleanliness and terminal seating, on passenger recommendations. This multifaceted approach offers robust insights into factors influencing airport recommendations and guides improvements in airport management to enhance passenger satisfaction. Future work will focus on a general-purpose machine learning framework and its toolbox development for airport service quality analysis based on online reviews from various sources. passenger satisfaction; time series; ANOVA; machine learning; interaction effects +
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influence passenger satisfaction. Skytrax is an international airline rating organization dedicated to improving customer experience quality for airlines and airports worldwide through surveys, service evaluations, etc. Ref. [14] confirmed the importance of signage and wayfinding based on the SERVQUAL model applied to survey data, aligning with previous studies based on Skytrax data. Here, SERVQUAL is an approach for measuring service quality. Ref. [7] focused on measurements used in airport service quality (ASQ) studies and introduced a list of services for sentiment analysis. This list aligns with findings from other studies [2; 15; 16; 17]. The study concluded that the most explored ASQ dimensions were servicescape, facilities, information, security, and check-in, while less explored ones included access, comfort, convenience, ticketing, function, and ICQ (Immigration, Customs, and Quarantine). Ref. [18] used a multi-criteria decision-making approach to identify factors impacting ASQ. The research found that positive sentiment was highly connected to the efficiency of services such as baggage delivery time, up-to-date aircraft, in-flight facilities, etc. However, the study mixed airport and airline services without clearly distinguishing between them. Furthermore, other directions are also taken for the study of ASQ. Ref. [19] studied the evolution of ASQ research and the measurement of passenger satisfaction using a small sample of 27 articles from 2000 to 2020. They found discrepancies between ASQ research and its application in the airport industry. Ref. [11] conducted a bibliometric analysis of ASQ studies from 1975 to 2020, revealing that most studies focused on passenger satisfaction and the impact of ASQ. Ref. [20] targeted specific services and passenger groups such as the elderly, but the data used were limited. Ref. [21] found few studies using sentiment analysis and machine learning for ASQ, suggesting further investigation necessary in these areas. This study identifies gaps in ASQ analysis and airport recommendation and develops a machine learning pipeline based on an integrated and comparative approach to study ASQ by service and forecast airport recommendations. More specifically, it applies statistical and predictive analysis to Skytrax passenger reviews.