COMPREHENSIVE DATA ANALYTICS STUDY OF THE AIRLINE INDUSTRY FROM 2012–2020 USING TABLE AU

The airline industry is increasingly leveraging data-driven techniques to improve customer satisfaction, operational efficiency, and sustainability. Several studies focus on utilizing text mining, sentiment analysis, and machine learning to analyze customer feedback from social media platforms, such as Twitter, to better understand passenger experiences. Hybrid models combining lexicon-based methods with deep learning have been proposed to enhance sentiment accuracy, while methodologies for analyzing flight delays, cancellations, and safety reports have been introduced to identify operational inefficiencies. Furthermore, sustainability practices in the airline industry are explored through machine learning clustering and bibliometric analysis, providing valuable insights for airlines to improve their environmental performance. This study also highlights the importance of integrating structured flight operation data with customer sentiment data for more comprehensive insights. Additionally, the use of Tableau facilitates realtime visual storytelling, allowing stakeholders to make faster and more informed decisions. Overall, the integration of advanced data analysis methods offers significant potential to enhance airline operations and customer service while addressing sustainability challenges.