Abstract Online user-generated content provides a valuable source for identifying dimensions of services. This study proposes a framework for extracting the dimensions of consumer satisfaction of public transportation services using unsupervised latent Dirichlet allocation model. A pilot study was performed on 17,747 online user reviews collected from 1452 public transportation agencies (including streetcar, light rail, heavy rail, boat, and aerial tram) in the United States over 8 years. The proposed approach is able to identify a few dimensions that were not discussed in the previous literature. This research also provides an alternative method to collectively gather users’ feedback and efficiently pre-process textual data related to transit customer satisfaction.


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    Titel :

    Public Transit Customer Satisfaction Dimensions Discovery from Online Reviews


    Beteiligte:
    Gao, Lu (Autor:in) / Yu, Yao (Autor:in) / Liang, Wuling (Autor:in)

    Erschienen in:

    Urban Rail Transit ; 2 , 3 ; 146-152


    Erscheinungsdatum :

    11.10.2016


    Format / Umfang :

    7 pages




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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