Event Detection Optimization Through Stacking Ensemble and BERT Fine-tuning For Dynamic Pricing of Airline Tickets

Dynamic pricing of airline tickets in competitive markets requires innovation that responds to market changes. Dynamic pricing is also influenced by public events, such as sporting events, music concerts, and more. This study aims to increase airline ticket revenue by optimizing flight occupancy during events. The data used is obtained from social media platform Twitter (X), with eight event classifications: soccer events, music concerts, volcanic eruptions, earthquakes, riots, floods, motorcycle racing, and others (non-events). We used a stacking ensemble method for data labeling and fine-tuned the BERT model for event detection. The stacking ensemble method achieved an accuracy rate of 0.99, while the fine-tuned BERT model produced an accuracy rate of 0.94. These results show a significant contribution to improving the accuracy and effectiveness of dynamic pricing. These findings not only offer a solution to the dynamic pricing challenge but also open opportunities to increase revenue by understanding event sentiment, providing competitiveness and flexibility in a dynamic market. With a focus on accurate event detection, this research paves the way for the development of more intelligent and adaptive dynamic pricing models by combining the strengths of the Stacking Ensemble labeling technique and BERT model fine-tuning to improve model accuracy.