## 5 Conclusions and Future Work  
This study provides a comprehensive analysis of airport service quality and its impact on passenger recommendations based on a well-designed machine learning pipeline and collected Skytrax online reviews. Employing various analytical techniques, including descriptive analysis, correlation analysis, ANOVA tests, predictive modelling and interaction effects, the research identifies key factors influencing passenger satisfaction and recommendations. The time series study in descriptive analysis indicates that the quality of airport services changes over time, with passengers showing more negative ratings in recent years compared to the past for all airport services. However, it must be noted that a significant number of passengers have not rated all elements of the evaluation matrix provided by Skytrax. Furthermore, the analysis of common keywords in passengers' reviews reveals several aspects of the airport experience that are not covered by the evaluation matrix but are of significant concern to passengers, such as passenger management and passport control. The correlation analysis between airport features and ratings and the likelihood of an airport being recommended by reviewers indicates a positive correlation at different levels. It also shows the alignment between the sentiment expressed in the passengers' review words and the numerical ratings. The ANOVA test results suggest that there are statistically significant differences in airport service ratings across years, except for airport shopping in the pre-2019 years. This implies that perceptions, and perhaps the actual quality, of services and facilities at airports are changing over time. This research also employed a logistic regression model to assess the impact of various airport services on the likelihood of an airport being recommended by passengers. The model achieved a weighted average precision of 84%. Terminal cleanliness, airport staff, and airport shopping were identified as the top three factors with a positive impact on the likelihood of an airport being recommended. Then, multiple machine learning models, including logistic regression, Random Forest, SVM, KNN, Gradient Boosting, and Neural Networks, were developed and cross-validated for airport recommendation based on Skytrax's online reviews. Among these models, Gradient Boosting emerged as the most accurate with a mean accuracy of 88.15%. The interaction effects analysis reveals that certain combinations of features, specifically terminal cleanliness with terminal seating, and terminal signs with airport staff, significantly and positively influence the likelihood of an airport being recommended. However, terminal cleanliness with food and beverages, and terminal signs with airport shopping, have a negative impact on the recommendation. Interaction effects analysis underscored the importance of combined service features in enhancing recommendations. The findings suggest that maintaining high standards in terminal cleanliness, staff service, and shopping facilities is crucial for positive passenger experiences. Conversely, some combinational services, such as food and beverages, require further investigation for their negative impact on recommendations. Overall, this study provides actionable insights for airport management to prioritize improvements and ensure a comprehensive enhancement of passenger experience, thereby increasing the likelihood of positive recommendations. Future work will focus on a general-purpose machine learning framework and its toolbox development for airport service quality evaluation using statistical, predictive, and comparative analysis, applicable to online reviews of various sources. Conceptualization, J.L. ; Methodology, M.S.M.A. and J.L. ; Software, M.S.M.A. ; Formal analysis, M.S.M.A. ; Resources, K.W.J. ; Data curation, M.S.M.A. ; Writing--original draft, M.S.M.A. and J.L. ; Writing--review & editing, J.L. ; Supervision, J.L. and K.W.J. ; Project administration, J.L. and K.W.J. ; Funding acquisition, K.W.J.