Service Quality Improvement of Electric Vehicle Timeshare Rental Project Based on Text Mining Technology

Abstract:

To address the issue of service quality improvement in electric vehicle （EV） timeshare rental projects in the era of big data， this paper， by combining text mining techniques with quality function deployment （QFD）， identifies customer demands and improves the service quality of EV rental enterprises， taking EVCARD， a typical Shanghai-based company， as a case study. First， customer review data were collected， segmented， and tagged for parts of speech. Using regular expressions， feature-emotion word pairs were extracted， their word frequency and comprehensive sentiment values were calculated， and eight customer demands requiring improvement were identified. Based on these findings， the QFD tool was applied， and a method combining customer demand attention with comprehensive sentiment values was used to determine customer demand weights in the construction of the left wall of the quality house. Subsequently， through literature research and expert interviews， 17 service quality elements were identified， ranked， and classified. Finally. recommendations for service quality improvement， such as customized service systems， were proposed for enterprises.