## 1 Introduction  
Air travel has undergone a remarkable transformation over the years, evolving from an emb of luxury to an accessible mode of global transportation. It stands as a testament to human ingenuity, facilitating global connectivity while adhering to stringent safety protocols [1]. The aviation revolution has fundamentally transformed spatial mobility and catalyzed significant shifts in international commerce. Nations with a strong aerospace presence enjoy benefits like boosted tourism, enhanced employment opportunities, technological advancements, and overall economic prosperity [2; 3]. A comprehensive study of airline markets is essential, which benefits not only frequent flyers and industry stakeholders but broader intellectual and economic spheres [4; 5]. However, forecasting airfares continues to pose significant difficulties [4; 6]. The dynamics of travel patterns are characterized by continual fluctuations, which ultimately impact the variability of ticket prices [7]. Airlines face the challenge of striking a balance between maximizing the profits and managing the changes of demand since the travelers are constantly seeking for cost-effective options and availability [8; 9; 10]. The convergence of these diverse interests highlights the importance of comprehending the mechanics of ticket pricing. Therefore, this study tries to explore the intricacies associated with the prediction of airplane ticket fare to gain comprehensive business insights. On the other hand, understanding customers' viewpoints holds the utmost significance in formulating price prediction methods, particularly within the aviation industry [11]. The dissemination of opinions on social networks significantly influences the purchase patterns [12]. However, traditional forecasting techniques, which heavily rely on the pricing data impacted by such factors as oil prices, seat demand, seasonality, and rival pricing tactics, sometimes fail to consider the influence of the digital footprints [4]. Though such factors have undeniable influences on the pricing dynamics, it is in fact not easy to formulate them precisely in the predictive models. However, the digital footprints are openly accessible from social media, and accordingly, it is possible to understand the customer attitudes through sentiment analysis, which plays a crucial role in enhancing the price prediction models and assures the attitudes align with real market sentiments [13]. Sentiment analysis, which examines customer views across various platforms, has increasingly garnered attention and recognition [14; 15]. Within this domain, ABSA has emerged as a powerful technique that provides a nuanced analysis on the feelings directed towards specific features of a service [16]. In this paper, instead of using manually defined aspects, the sentiment analysis first makes use of a BERT model to automatically extract the aspects of airline service such as safety, seat, comfort, food, airport, baggage, customer service, etc., from the customer reviews. A few aspects were then selected and categorized into nine related aspect groups according to the airline management point of view, based on which sensitivity analysis was conducted to assess the customer attitudes for individual groups. These efforts play a crucial role in enriching the understanding of customer perspectives, which is essential for the success of airline business. Additionally, a hybrid model was proposed to combine the aspect-based sentiment analysis (ABSA) with historical ticket transaction data, specifically leveraging the capabilities of the gated recurrent unit (GRU) model that has shown impressive progress in modeling sequential data [17]. Inclusion of the ABSA can expand the information scope of the models and ensure a more precise depiction of market attitudes when performing predictions.