Social Media Mining on Taipei's Mass Rapid Transit Station Services based on Visual-Semantic Deep Learning

doi:10.37394/23205.2022.21.16

淡江大學機構典藏 > 商管學院 > 運輸管理學系暨研究所 > 期刊論文 > Item 987654321/123424

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题名:	Social Media Mining on Taipei's Mass Rapid Transit Station Services based on Visual-Semantic Deep Learning
作者:	TAO, Chi-Chung;CHEUNG, Yue-Lang Jonathan
关键词:	social media analytics;opinion mining;visual semantic;deep learning;Taipei MRT station services;quality assessment
日期:	2022-03-31
上传时间:	2023-04-28 18:03:10 (UTC+8)
出版者:	World Scientific and Engineering Academy and Society (W S E A S)
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.
關聯:	WSEAS TRANSACTIONS on COMPUTERS 21, p.110-117
DOI:	10.37394/23205.2022.21.16
显示于类别:	[運輸管理學系暨研究所] 期刊論文

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