出版日期：2022-03-31 00:00:00

著者：Chi-Chung TAO; Yue-Lang Jonathan CHEUNG

著錄名稱、卷期、頁數：WSEAS TRANSACTIONS on COMPUTERS 21, p.110-117

摘要：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.

關鍵字：social media analytics; opinion mining; visual semantic; deep learning; Taipei MRT station services; quality assessment

語言：en

ISSN：2224-2872; 1109-2750

期刊性質：國外

收錄於：EI Scopus

通訊作者：Chi-Chung TAO

審稿制度：是

國別：GRC

出版型式：電子版,紙本