## 3 Methodology
### Data Preprocessing
After preparing the airline fare data, our focus was redirected toward the analysis of the customer review data to explore the attitudes and perspectives of the travelers, thereby providing a complete comprehension of the airline sector from both the ticket transactions and the customer opinions. Figure 1 depicts the workflow of processing the customer reviews to extract the aspects and evaluate the sentiment. The workflow contains mainly three stages. In the first stage of tokenization and screening, the raw texts of customer reviews are tokenized into identifiable words or symbols one by one, and the stop words as well as those terms with less impact on the semantics are removed. Table 3 shows two sample reviews before and after tokenization and screening. As can be observed from Table 3, such stop words as \"I\", \"the\", \"and\", \"by\", and \"it\" are removed. Following this, all the cleaned texts are collected and passed further to the stage of aspect extraction, which makes use of a pre-trained BERT model, which is specially customized for aspect-based tasks, to extract the aspect words. Aspect words are essential for the sentiment analysis since the customer opinions may be well categorized according to the aspects of the services. Afterwards, the extracted aspect words are utilized in the stage of sentiment evaluation to obtain a sentiment score for every review according to the aspect words within it. Figure 2 offers a visual representation of the predominant aspect words extracted from customer reviews, presented through a word cloud. This visualization method not only highlights the relative frequency of each term by adjusting its font size but also serves as an immediate visual summary of passenger priorities and concerns. The prominence of certain aspects within the word cloud indicates their frequent mention across the dataset, suggesting that addressing these areas could significantly enhance customer satisfaction. By focusing on these high-frequency terms, airline service providers can align their service improvements more closely with customer expectations. Furthermore, this word cloud enables quick identification of the most discussed service aspects in passenger reviews, providing actionable insights that are supported by quantitative data analysis. These insights are essential for management to effectively prioritize resource allocation toward areas that will most improve service quality. \begin{table}
\begin{tabular}{l l l} \hline \hline
**Dataset** & **Records** & **Percentage** \\ \hline Training set & 672,126 & 80\% \\ Validation set & 84,016 & 10\% \\ Test set & 84,016 & 10\% \\ \hline \hline \end{tabular}
\end{table} 
Table 1: A distribution of airline fare transaction across dataset splits. \begin{table}
\begin{tabular}{l l l} \hline \hline
**Feature** & **Description** & **Possible Values** \\ \hline Travel Date & Date of the scheduled flight & YY/MM/DD \\ Booking Class & Code representing fare type and associated restrictions & A, B, C, D,... Z \\ Class of Service & Level of service for the flight & Economy, Business, First class \\ Seg Orig. & The starting point of the travel segment & Airport’s code \\ Seg Dest. & The endpoint of the travel segment & Airport’s code \\ Distance & Distance between the departure and arrival airports & Miles/Kilometers \\ Season & Time of year, such as summer, winter, autumn, or spring, when travel occurs & Summer/Winter/Autumn/Spring \\ Point of Ticket Issuance & The location where the ticket is purchased & Location/Office/Platform name \\ Luggage & Policies on allowed luggage & Yes/1–2, No/0 \\ Overnight Flight & Whether the flight travels overnight & Yes/No \\ Actual Fare & Final ticket cost & USD \\ \hline \hline \end{tabular}
\end{table} 
Table 2: A few sample features employed in the flight fare forecasting model. Figure 1: Workflow for customer review processing, aspect extraction, and sentiment scoring.