For public transport operators, passengers’ comments towards their experience are valuable for promoting more friendly transportation services. This paper demonstrates that passenger-generated online comments can be used to assess railway transportation station services. The natural language processing and social media mining techniques that include establishing an opinion classification model through visual semantic fusion deep learning methods are applied to assess Taipei’s Mass Rapid Transit (MRT) station services from the internet opinions. An opinion monitoring system includes: (1) opinion mining to build a social media comment dataset on the ontology of MRT stations.; (2) proposing intent-sentiment, image-text relationship, and content type categories to assist accessing of passengers’ quality of experience; (3) constructing a classification model to classify the nature of opinions (4) proposing visualization to provide an intuitive information display dashboard to help Taipei’s MRT operator sense the sentiment-intention trends of comments on each station and access the current service level as well as part of the quality management assessment is also proposed.
This paper firstly describes key issues of developing a web-based GIST data warehouse system to lay a solid foundation for value-added applications. An integrated concept is then presented to ensure previous data warehouse system can be transformed into a web-based one. The system architecture of the proposed data warehouse system is designed for user interface end, system end, data end and web service end. To demonstrate system performance, metadata search function is taken as an example. Finally, a framework of the integrated tourism information system is proposed as a value-added application.
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