## 3 Methodology
### Aspect-Based Sentiment Analysis  
Aspect-based sentiment analysis (ABSA) is the analysis method that extracts specific attributes or features, called aspects, from textual data and independently assesses the sentiment associated with each of them to gain more insights into customer opinions [24].The field of sentiment analysis categorizes every review into positive, negative, or neutral attitudes, offering a comprehensive approach to understanding customer opinions [25; 26; 27]. Unlike traditional sentiment analysis, ABSA delves into the specific characteristics of a product or service [28]. For example, in hotel reviews analyzed in the research, aspects such as comfort, price, facility, traffic, and service are frequently discussed. A review might state, \"The hotel's location was incredibly convenient, but the room price was unexpectedly high\" [29]. Here, ABSA would identify a positive sentiment toward the aspect of location (convenient) but a negative sentiment toward price (high). This detailed method enables a deeper understanding of customer perspectives, capturing nuanced views that might be overlooked in general sentiment analysis [29]. ABSA is particularly beneficial in the scenarios where various product or service aspects significantly influence overall customer perception and satisfaction [30; 31].  
ABSA starts from pinpointing specific aspects or features mentioned in textual reviews, such as comfort, customer, and other services in the airline industry. After identifying these aspects, ABSA assigns a sentiment polarity to each aspect individually based on the context in which it is mentioned [32; 33] instead of labeling an entire review with merely one sentiment score. In this way, ABSA can illuminate those aspects directly influencing the customers' perceptions and offer a more detailed understanding of areas for enhancement or marketing emphasis [34; 35; 36].  
In predicting airline fares, the typical factors, including season, travel day and month, weekend, holiday, booking class, class of service, and number of stops, are taken into consideration [6; 9; 18]. However, passenger sentiments on key services may also affect the ticket prices [37; 38; 39]. With ABSA, airlines can discern the specific service attributes that might determine travelers' decisions of expenditure [39; 40]. By combining this knowledge with historical fare data, the prediction models may become more refined and can capture those aspects of passengers' experiences that influence fare choices.  
In ABSA, each review in the dataset might touch upon multiple aspects of the airline service, and the sentiment score for a distinct aspect \(a_{i}\) is denoted as \(s(a_{i})\), of which the value ranges from \(-\)1 to 1, and its sign denotes the polarity. For a review indexed by \(l\), the sentiment score \(s_{l}\) is computed as follows:  
\[s_{l}=\sum\
olimits_{a_{i}\in A_{l}}w_{i}s(a_{i}) \tag{2}\]  
\[s(a_{i})=n_{+}(a_{i})-n_{-}(a_{i}) \tag{3}\]  
where \(A_{l}\) is the set containing all the aspects present in the review, \(w_{i}\) is the weight assigned to the aspect \(a_{i}\), and \(n_{+}(a_{i})\) and \(n_{-}(a_{i})\) are the positive and negative evidences of \(a_{i}\), respectively.