Exploring the impact of COVID-19 on aviation industry: A text mining approach

• Main Authors: GOTTIPATI, Swapna, SHIM, Kyong Jin, JIANG, Weiling Angeline, LEE, Sheng Wei Andre Justin
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2020
• Subjects: analytics; COVID-19; text mining; aviation industry; Databases and Information Systems
• Online Access: https://ink.library.smu.edu.sg/sis_research/5540; https://ink.library.smu.edu.sg/cgi/viewcontent.cgi?article=6543&context=sis_research
• Institution: Singapore Management University

Our study presents a comprehensive analysis of news articles from FlightGlobal website during the first half of 2020. Our analyses reveal useful insights on themes and trends concerning the aviation industry during the COVID-19 period. We applied text mining and NLP techniques to analyse the articles for extracting the aviation themes and article sentiments (positive and negative). Our results show that there is a variation in the sentiment trends for themes aligned with the real-world developments of the pandemic. The article sentiment analysis can offer industry players a quick sense of the nature of developments in the industry. Our article theme analysis adds further value by summarizing the common key topics within the positive and negative corpora, allowing stakeholders in the aviation industry to gain more insights on areas of concerns or aspects that are affected by the pandemic.