Transforming the Performance of Airline Industry Through Sentiment Analysis

Khan, Mukarrum Ali (2024) Transforming the Performance of Airline Industry Through Sentiment Analysis. Masters thesis, Dublin, National College of Ireland.

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Abstract

The rapid growth in technology-oriented businesses have encouraged different industries to adopt modern approaches such as sentiment analysis for better understanding of their customers to help gauge their feelings regarding the provided service. This research paper focuses on gathering meaningful insights from airline company customers through sentiment analysis which can help transform the overall performance within the airline industry. This research employs machine learning techniques such as Random Forest and Naïve Bayes to critically assess customer sentiments based on airline company’s dataset sourced through Kaggle. The used dataset in this research focuses on several aspects of airline services offered throughout the journey and provides customer ratings on its key factors. The models were evaluated through metrics such as accuracy, precision, recall, ROC curve and AUC score. Random Forest outperformed Naïve Bayes with an accuracy of 89.1% and an AUC of 95.1% compared to 79.3% accuracy rate and 89.3% AUC of Naïve Bayes. Correlation through weights highlighted the key factors that airline industry must focus on to transform the performance and enhance user experience. In-flight entertainment, ease of online booking and online customer support were the key factors that derive most customer satisfaction. This research highlighted actionable insights for the airline industry through the effective deployment of machine learning techniques in sentiment analysis. These insights can help airline companies in enhancing user experience, retaining customer loyalty and create a competitive advantage to capture market share and overall transform the performance of their business.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Haque, Rejwanul UNSPECIFIED
Uncontrolled Keywords:	Sentiment Analysis; Machine Learning; Airlines Industry; Random Forest; Naïve Bayes; Social Media
Subjects:	Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science H Social Sciences > HD Industries. Land use. Labor > Specific Industries > Aviation Industry H Social Sciences > HF Commerce > Marketing > Consumer Behaviour Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning Z Bibliography. Library Science. Information Resources > ZA Information resources > ZA4150 Computer Network Resources > The Internet > World Wide Web > Websites > Online social networks T Technology > TK Electrical engineering. Electronics. Nuclear engineering > Telecommunications > The Internet > World Wide Web > Websites > Online social networks
Divisions:	School of Computing > Master of Science in Artificial Intelligence for Business
Depositing User:	Ciara O'Brien
Date Deposited:	02 Jul 2025 14:45
Last Modified:	02 Jul 2025 14:45
URI:	https://norma.ncirl.ie/id/eprint/7988

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