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Article

Mining Public Opinion on Transportation Systems Based on Social Media Data

by 1,2,*, 1,2,* and 3
1
Jiangsu Key Laboratory of Urban ITS, School of Transportation, Southeast University, Nanjing 210096, China
2
Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic, Southeast University, Nanjing 210096, China
3
China Academy of Transportation Sciences, No.240, Huixinli, Chaoyang District, Beijing 100029, China
*
Authors to whom correspondence should be addressed.
Sustainability 2019, 11(15), 4016; https://doi.org/10.3390/su11154016
Received: 10 June 2019 / Revised: 12 July 2019 / Accepted: 17 July 2019 / Published: 25 July 2019
(This article belongs to the Special Issue Sustainable and Intelligent Transportation Systems)
Public participation plays an important role of traffic planning and management, but it is a great challenge to collect and analyze public opinions for traffic problems on a large scale under traditional methods. Traffic management departments should appropriately adopt public opinions in order to formulate scientific and reasonable regulations and policies. At present, while increasing degree of public participation, data collection and processing should be accelerated to make up for the shortcomings of traditional planning. This paper focuses on text analysis using large data with temporal and spatial attributes of social network platform. Web crawler technology is used to obtain traffic-related text in mainstream social platforms. After basic treatment, the emotional tendency of the text is analyzed. Then, based on the probabilistic topic modeling (latent Dirichlet allocation model), the main opinions of the public are extracted, and the spatial and temporal characteristics of the data are summarized. Taking Nanjing Metro as an example, the existing problems are summarized from the public opinions and improvement measures are put forward, which proves the feasibility of providing technical support for public participation in public transport with social media big data. View Full-Text
Keywords: traffic planning; public participation; big data; content analysis; spatiotemporal properties traffic planning; public participation; big data; content analysis; spatiotemporal properties
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MDPI and ACS Style

Li, D.; Zhang, Y.; Li, C. Mining Public Opinion on Transportation Systems Based on Social Media Data. Sustainability 2019, 11, 4016. https://doi.org/10.3390/su11154016

AMA Style

Li D, Zhang Y, Li C. Mining Public Opinion on Transportation Systems Based on Social Media Data. Sustainability. 2019; 11(15):4016. https://doi.org/10.3390/su11154016

Chicago/Turabian Style

Li, Dawei, Yujia Zhang, and Cheng Li. 2019. "Mining Public Opinion on Transportation Systems Based on Social Media Data" Sustainability 11, no. 15: 4016. https://doi.org/10.3390/su11154016

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