The airline industry faces the critical challenge of meeting increasing passenger expectations amidst rapid technological advancements and intense competition. To remain competitive, airlines must gain a deeper understanding of passenger satisfaction and use this knowledge to improve service quality. This paper addresses this challenge by leveraging online customer reviews to derive actionable insights into passenger experiences. We introduce AIRNODE (Attention-based Insights for Reviewing Node Optimized Destinations and Experiences), a comprehensive two-stage model designed to analyze these reviews. AIRNODE constructs a weighted graph to aggregate review data and utilizes a Graph Attention Network (GAT) to model complex spatial relationships between destinations, achieving 84% accuracy in classifying destinations based on aggregated user satisfaction. Through advanced keyword extraction, we identify key aspects such as customer service, delays, and staff behavior, providing deep insights into the factors that drive passenger satisfaction. Case studies highlight destinations with varying levels of satisfaction, identifying positive attributes and areas needing improvement, and offering detailed insights and justifications for enhancing customer satisfaction. These insights equip airlines with a data-driven strategy to enhance service quality, meet traveler expectations, and maintain a competitive edge in a dynamic market.