Determination of Service Quality Dimensions of Online Complaints in Road Passenger Transport by Text Mining

Customer feedback is a critical asset for navigating competitive markets. This study aims to analyze online customer complaints in the road passenger transport sector to identify service quality dimensions and pinpoint recurring industry issues, thereby offering strategic insights for enhancing customer satisfaction. The research used a comprehensive dataset of 7,719 complaints regarding the three most preferred transport companies in Turkey, retrieved from www.sikayetvar.com. Data analysis was conducted using Python-based text mining techniques, specifically latent Dirichlet allocation (LDA) for unsupervised topic modeling and classification. The findings revealed that complaints primarily focused on physical characteristics, trust-related issues, and the physical conditions of vehicles. Comparative analysis showed that while Company K and Company P received significant complaints in the "Tangibility" dimension (30.46% and 24.4%, respectively), Company M faced its highest complaint rate in the "Reliability" dimension with 58.93%. These results suggest that service quality gaps vary by company, requiring tailored improvements in physical assets or service reliability to boost overall passenger satisfaction.