## 3 Methodology
### Data Preprocessing
\begin{table}
\begin{tabular}{p{128.0 pt} p{128.0 pt}} \hline \hline
**Review Text** & **Cleaned Text after Tokenization and Screening** \\ \hline I appreciate the good and warm services provided by every staff ground and cabin. They make this my journey comfortable and relaxing. The money is worth it, the food and beverages were good too. The flight time is right on schedule. & [‘appreciate’, ‘good’, ‘warm’, ‘services’, ‘provided’, ‘every’, ‘staff’, ‘ground’, ‘cabin’, ‘make’, ‘journey’, ‘comfortable’, ‘relaxing’, ‘money’, ‘worth’, ‘food’, ‘beverages’, ‘good’, ‘flight’, ‘time’, ‘right’, ‘schedule’ \\ \hline Overall a good quality airline but not at the front. Flew A380 lower deck, aisle rear seating, reasonable experience, very smooth flight. Cabin clean and tidy, staff and service good, ground service also good. Onboard entertainment below par, selection reasonable but not up with some, food ordinary to average only, leg space and seating on lower level below average—tight between seats-limited leg/knee room. Having experienced the A380 I now prefer the Boeing 777 or 340. I spoke to friends who have flown upstairs on the A380 and thought it to be better option. & [‘Overall’, ‘good’, ‘quality’, ‘airline’, ‘front’, ‘Flew’, ‘A380’, ‘lower’, ‘deck’, ‘aisle’, ‘rear’, ‘seating’, ‘reasonable’, ‘experience’, ‘smooth’, ‘flight’, ‘Cabin’, ‘clean’, ‘tidy’, ‘staff’, ‘service’, ‘good’, ‘ground’, ‘service’, ‘also’, ‘good’, ‘Onboard’, ‘entertainment’, ‘par’, ‘selection’, ‘reasonable’, ‘food’, ‘ordinary’, ‘average’, ‘leg’, ‘space’, ‘seating’, ‘lower’, ‘level’, ‘average’, ‘tight’, ‘seats’, ‘limited’, ‘leg’, ‘knee’, ‘room’, ‘experienced’, ‘A380’, ‘prefer’, ‘Boeing’, ‘777’, ‘340’, ‘spoke’, ‘friends’, ‘flown’, ‘upstairs’, ‘A380’, ‘thought’, ‘better’, ‘option’ \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Sample reviews from the dataset before and after tokenization and screening. Figure 2: Word cloud visualization of predominant aspects in customer review. The selected sample aspects are summarized in Table 4 with a concise description of its significance for each. These aspects were used further in the sentiment evaluation since they are what the customers care about from the administrative point of view. Additionally, Figures 3 and 4 display correlation matrices that encompass both historical ticket fare data and various airline service aspects, respectively. Figure 3 focuses on booking and travel-related attributes such as holidays, booking class, and the point of ticket issuance, while Figure 4 delves into service aspects including safety, food, crew interaction, and comfort. Each matrix not only illustrates the strength and direction of relationships between pairs of features but also includes \(p\)-values to highlight the statistical significance of these correlations, thereby providing a more robust basis for data interpretation. The \(p\)-values are denoted within the matrices using a straightforward notation to simplify understanding: three asterisks (**) indicate a highly significant correlation with a \(p\)-value less than 0.0001, two asterisks (**) denote significance at the 0.01 level, one asterisk (*) signifies significance at the 0.05 level, and a dot (.) represents correlations that are not statistically significant.