Evaluation model of urban rail transit service quality based on social network data

The acquisition and satisfaction of passengers ’ perception is the key basis for improving the attractiveness of urban rail transit. The scientific evaluation process and accurate results will bring critical impacts to the optimization of urban rail transit operation management. To solve the problem that the absolute dependence of urban rail transit service quality on questionnaire surveys cannot reflect the real psychological perception of passengers, this paper used natural language processing to quantify the service quality of rail transit based on social network data. First, a web crawler was used to collect comment data in social networks, and an analysis method based on sentiment lexicon construction and degree division was adopted for determining the sentiment polarity and intensity of the text preprocessing results. Then, by establishing the evaluation index system of rail transit service quality based on K-Means text clustering, passengers ’ demands and service elements were transformed into evaluation indexes. The index weight was acquired by TF-IDF calculation of the importance of feature items, and the comprehensive score of service quality was obtained by combining the results. Finally, the corpus of Chongqing rail transit comments in Microblog was selected as the case for empirical analysis. The results prove that the service quality of Chongqing rail transit scores 4.383, which is generally at a low level. There is still much room for improvement in operation services. Passengers have the highest satisfaction with the intelligence of ticket checking and personnel services, and the lowest emotional score on compartment temperature. The key factor affecting service quality is safety (7.850%), followed by economical fares (7.524%), ticket convenience (7.212%), and intelligent ticket checking (7.139%). Compared with the existing methods, social network data can more intuitively identify passengers ’ opinions, providing a scientific data source for the evaluation of rail transit service quality. © 2023, Central South University Press. All rights reserved.