## 4 Results and Discussion  
In this section, we first investigate how ABSA could contribute to the prediction of flight ticket fare. This experiment utilized ninety-four input features drawn from both historical fare data and the airline sentiment data. Historical fare data included features such as travel date, booking class (ranging from A to Z), service class (economy, business, and first-class), origin, destination, distance, flight duration, number of stops, and season (summer, winter, autumn, and spring). The service aspects in the sentiment data include announcements, cleanliness, safety, baggage handling, staff interactions, cabin pressure, cancellation policies, security procedures, seat, comfort, crew, ambient noise, gate efficiency, rebooking processes, loyalty program benefits, legroom, air conditioning quality, boarding experience, and in-flight entertainment offerings. These features provide unique and valuable information that might be helpful in fare prediction. However, the many aspects might make the analysis results verbose and complicated, and make it difficult to obtain an overview on the service quality. To simplify the analysis, the aspects were therefore merged into nine distinct groups. _Booking and ticketing_ address the initial stages of a traveler's journey. _Pre-flight procedures_ involve the steps for boarding. _Airport services_ encompass the range of facilities and operations within the airport. _In-flight amenities and services_, along with _seat and cabin features_, ensure comfort and entertainment during the flight. The _staff_ category assesses the performance and interaction of airline personnel. _Safety and security_, together with _cleanliness_, are dedicated to maintaining high standards of safety and hygiene, respectively. Lastly, _post-flight services and issues_ deal with aspects of the journey after the aircraft lands, such as baggage claim, post-flight customer service, and handling flight-related issues. The grouping of aspects aims primarily to reduce the complexity such that the sentiment analysis results could be more compact, manageable, meaningful, and easy to visualize and interpret with clarity and relevance. In addition, the aspect groups could help to quickly identify the range of service aspects, so the airline company can improve the service quality more effectively. To provide a clear view of the sentiment data used in our analysis, Table 5 displays the sentiment scores across each aspect group. Figure 8 shows the yearly ABSA results presented based on the nine aspect groups from 2018 to 2023. The two colors signify the ratios of the positive and negative sentiments \begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Aspect Group** & **Positive** & **Negative** & **Training Set** & **Validation Set** & **Test Set** \\ \hline Staff & 18,028 & 14,532 & 26,048 & 3256 & 3256 \\ Safety and Security & 10,601 & 2108 & 10,167 & 1270 & 1272 \\ Seat and Cabin Features & 14,108 & 7478 & 17,269 & 2159 & 2159 \\ Pre-flight Procedures & 3562 & 3838 & 5920 & 740 & 740 \\ Post-flight Services and Issues & 2762 & 4854 & 6094 & 762 & 762 \\ In-flight Amenities and Services & 9156 & 2896 & 9642 & 1205 & 1205 \\ Cleanliness & 3586 & 3446 & 5626 & 703 & 703 \\ Booking and Ticketing & 3454 & 3362 & 5453 & 682 & 682 \\ Airport Services & 11,528 & 12,736 & 19,411 & 2426 & 2426 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Aspects group sentiment score. within the customer reviews for each aspect group denoting distinct service dimensions. Across the years, the persistence of positive feedback in certain categories, such as _safety and security_, suggests favorable passenger feedback or effective service strategies, while the presence of negative sentiment, such as _post-flight services and issues_, suggests unfavorable passenger feedback and the need for ongoing attention and enhancement.