Advanced Mathematical Models & Applications

This study investigates the dynamic relationship between airport pricing strategies and digital publicity of an airport, particularly focusing on the role of public sentiment. As competition among airports intensifies, understanding how pricing and airport brand appeal interact is essential for optimizing competitive strategies. While previous research has explored pricing dynamics and marketing in isolation, limited attention has been given to their coupled evolution, especially under the influence of public sentiment. To address this gap, we develop a non-linear dynamical systems model that links airport pricing with marketing intensity, incorporating sentiment as a key factor. The model is based on a set of coupled differential equations that describe the evolution of pricing and marketing efforts over time. We employ web scraping techniques powered by artificial intelligence (AI) tools to gather historical data on public sentiment and digital visibility, integrating this data with historical mean airport price data. Bifurcation and sentiment analysis reveal that public sentiment influences the interaction between pricing and marketing, with positive sentiment amplifying the feedback loop between the two. However, the lack of long-term cointegration between pricing and online airport mentions data suggests that sentiment-driven effects are temporary. This work contributes to the understanding of airport competitiveness by combining dynamical modelling with historical sentiment analysis.