Social Media Mining on Taipei's Mass Rapid Transit Station Services based on Visual-Semantic Deep Learning
學年 110
學期 2
出版（發表）日期 2022-03-31
作品名稱 Social Media Mining on Taipei's Mass Rapid Transit Station Services based on Visual-Semantic Deep Learning
作品名稱（其他語言）
著者 Chi-Chung TAO; Yue-Lang Jonathan CHEUNG
單位
出版者
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
產學合作
通訊作者 Chi-Chung TAO
審稿制度 是
國別 GRC
公開徵稿
出版型式 ,電子版,紙本
相關連結

機構典藏連結 ( http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/123424 )

SDGS 尊嚴就業與經濟發展,產業創新與基礎設施,永續城市與社區,夥伴關係