Next Article in Journal
Spatial–Temporal Evolution and Regional Differentiation Features of Urbanization in China from 2003 to 2013
Next Article in Special Issue
A Novel Method of Missing Road Generation in City Blocks Based on Big Mobile Navigation Trajectory Data
Previous Article in Journal
Multisource Hyperspectral and LiDAR Data Fusion for Urban Land-Use Mapping based on a Modified Two-Branch Convolutional Neural Network
Previous Article in Special Issue
A Task-Oriented Knowledge Base for Geospatial Problem-Solving
Open AccessArticle

Social Media Big Data Mining and Spatio-Temporal Analysis on Public Emotions for Disaster Mitigation

1
Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China
2
University of Chinese Academy of Sciences, Beijing 100094, China
3
College of Geomatics, Shandong University of Science and Technology, Qingdao 266590, China
*
Author to whom correspondence should be addressed.
ISPRS Int. J. Geo-Inf. 2019, 8(1), 29; https://doi.org/10.3390/ijgi8010029
Received: 30 October 2018 / Revised: 21 December 2018 / Accepted: 10 January 2019 / Published: 15 January 2019
(This article belongs to the Special Issue Big Data Computing for Geospatial Applications)
Social media contains a lot of geographic information and has been one of the more important data sources for hazard mitigation. Compared with the traditional means of disaster-related geographic information collection methods, social media has the characteristics of real-time information provision and low cost. Due to the development of big data mining technologies, it is now easier to extract useful disaster-related geographic information from social media big data. Additionally, many researchers have used related technology to study social media for disaster mitigation. However, few researchers have considered the extraction of public emotions (especially fine-grained emotions) as an attribute of disaster-related geographic information to aid in disaster mitigation. Combined with the powerful spatio-temporal analysis capabilities of geographical information systems (GISs), the public emotional information contained in social media could help us to understand disasters in more detail than can be obtained from traditional methods. However, the social media data is quite complex and fragmented, both in terms of format and semantics, especially for Chinese social media. Therefore, a more efficient algorithm is needed. In this paper, we consider the earthquake that happened in Ya’an, China in 2013 as a case study and introduce the deep learning method to extract fine-grained public emotional information from Chinese social media big data to assist in disaster analysis. By combining this with other geographic information data (such population density distribution data, POI (point of interest) data, etc.), we can further assist in the assessment of affected populations, explore emotional movement law, and optimize disaster mitigation strategies. View Full-Text
Keywords: social media; big data; fine-grained emotion classification; spatio-temporal analysis; hazard mitigation social media; big data; fine-grained emotion classification; spatio-temporal analysis; hazard mitigation
Show Figures

Figure 1

MDPI and ACS Style

Yang, T.; Xie, J.; Li, G.; Mou, N.; Li, Z.; Tian, C.; Zhao, J. Social Media Big Data Mining and Spatio-Temporal Analysis on Public Emotions for Disaster Mitigation. ISPRS Int. J. Geo-Inf. 2019, 8, 29. https://doi.org/10.3390/ijgi8010029

AMA Style

Yang T, Xie J, Li G, Mou N, Li Z, Tian C, Zhao J. Social Media Big Data Mining and Spatio-Temporal Analysis on Public Emotions for Disaster Mitigation. ISPRS International Journal of Geo-Information. 2019; 8(1):29. https://doi.org/10.3390/ijgi8010029

Chicago/Turabian Style

Yang, Tengfei; Xie, Jibo; Li, Guoqing; Mou, Naixia; Li, Zhenyu; Tian, Chuanzhao; Zhao, Jing. 2019. "Social Media Big Data Mining and Spatio-Temporal Analysis on Public Emotions for Disaster Mitigation" ISPRS Int. J. Geo-Inf. 8, no. 1: 29. https://doi.org/10.3390/ijgi8010029

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop