A Text Mining Study of Airline ESG

As environmental and social responsibilities gain prominence, the aviation industry faces growing pressure to adopt and communicate robust Environmental, Social, and Governance (ESG) strategies. Despite this, limited research explores how airlines address ESG themes within diverse regulatory and cultural contexts, underscoring the need for tailored approaches that align with global standards and local expectations—crucial for effective stakeholder engagement and accountability in a high-impact industry. This study aims to analyze and compare the ESG strategies of three major airlines—Korean Air, American Airlines, and Singapore Airlines—by examining their ESG reports from 2020 to 2023. Using Latent Dirichlet Allocation (LDA) and Semantic Network Analysis (SNA), the research identifies key ESG themes and investigates how these themes are influenced by each airline’s regulatory and operational environments. Findings reveal shared priorities across the airlines, such as sustainability, employee well-being, safety, and governance. However, each airline demonstrates unique emphases: Korean Air aligns environmental metrics with financial performance, American Airlines focuses on data-driven transparency, and Singapore Airlines integrates community and governance. These contextual variations illustrate how regional factors shape ESG strategies in the aviation sector. The study enriches ESG communication literature by providing a replicable text-mining framework for industry-specific ESG analysis, addressing calls for empirical, data-driven insights. Practically, findings support airline executives and policymakers in designing ESG frameworks that foster stakeholder trust and resilience by balancing global standards with regional needs, reinforcing the role of ESG as a strategic asset in aviation.