## 2 Literature Review
### Literature Review from the Perspective of Online-Based Passengers' Feedback  
To save on the cost and time associated with surveying passengers, analysing online comments using topic modelling methods, such as Latent Dirichlet Allocation (LDA), has become a popular approach for studying airport services [9; 36; 37]. These methods, combined with dimension reduction approaches, are applied to identify a limited number of topics from passengers' comments. Such multi-group analysis can reveal differences in the sentimental status of various passenger groups. For example, Ref. [1] found that business passengers and couple leisure passengers shared similar opinions on ASQ and were the most vocal in expressing their opinions. Both groups tend to give lower ratings and more negative sentiments in their reviews compared to family and solo passengers. The couple leisure group, in particular, has the highest number of negative reviews, highlighting the need for improvement [38]. Research by [8] indicates that frequent travelling passengers generally show a different level of satisfaction than the others. They are more satisfied overall, but all passengers are dissatisfied with the prices of services at airports. Ref. [28] used data from Google Maps to investigate changes in sentimental status during COVID-19, showing no significant impact on ratings. Ref. [36] applied LDA to predict airport star ratings from sentiment scores. Ref. [12] used content analysis to distinguish between satisfied and dissatisfied passengers based on their online reviews. They found that dissatisfied passengers frequently used words such as \"security\", \"check\", \"staff\", \"flight\", and \"line\", while satisfied passengers often used terms such as \"staff\", \"terminal\", \"time\", \"clean\", \"immigration\", and \"free\". Ref. [17] employed Data Mining to measure ASQ by analysing London Heathrow Airport's Twitter dataset, revealing significant differences in the frequency of references to services such as ground transport and waiting.  
In recent years, machine learning, particularly supervised methods such as Deep Learning, has gained popularity for predicting passenger sentiment. Ref. [5] used social media data to predict sentiment based on Vader [39] and LSVA [40]. They found that the quality of airport services can be measured by sentiments associated with services such as access, check-in/security, wayfinding, facilities, airport environment, and staff. Ref. [41] applied SERVQUAL to two airports in Nigeria without focusing on specific service types. It was found that passengers generally had high expectations and low service ratings. Ref. [2] used thousands of English and Arabic tweets to train CNN and LSTM models to predict positive or negative passenger sentiments toward airport services. The LSTM model performed better, though the difference was not significant. This approach, however, is more technique-oriented and offers limited emotion categories and applications [37]. For instance, understanding specific negative emotions such as sadness, fear, anger, or disgust can be more practical. Ref. [42] evaluated several Deep Learning architectures with different datasets and found that the best performance was achieved when LSTM and CNN were combined.  
The review shows that the topic modelling is frequently employed to analyse online reviews written by passengers, to identify specific airport services and differences in sentiment among various passenger groups. While machine learning and Deep Learning techniques have been applied in some studies, these approaches are still relatively limited in their application to ASQ and passengers' sentimental values using online reviews [43].