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Article

Temporal and Spatial Evolution and Influencing Factors of Public Sentiment in Natural Disasters—A Case Study of Typhoon Haiyan

by 1,2,3 and 1,2,3,4,*
1
Key Laboratory of Environmental Change and Natural Disaster, Beijing Normal University, Beijing 100875, China
2
State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, Beijing 100875, China
3
Faculty of Geographical Science, Center for Geodata and Analysis, Beijing Normal University, Beijing 100875, China
4
National Tibetan Plateau Data Center, Beijing 100101, China
*
Author to whom correspondence should be addressed.
Academic Editors: Wolfgang Kainz and Yeran Sun
ISPRS Int. J. Geo-Inf. 2021, 10(5), 299; https://doi.org/10.3390/ijgi10050299
Received: 26 March 2021 / Revised: 25 April 2021 / Accepted: 30 April 2021 / Published: 5 May 2021
(This article belongs to the Special Issue Geovisualization and Social Media)
The public’s attitudes, emotions, and opinions reflect the state of society to a certain extent. Understanding the state and trends of public sentiment and effectively guiding the direction of sentiment are essential for maintaining social stability during disasters. Social media data have become the most effective resource for studying public sentiment. The TextBlob tool is used to calculate the sentiment value of tweets, and this research analyzed the public’s sentiment state during Typhoon Haiyan, used the biterm topic model (BTM) to classify topics, explored the changing process of public discussion topics at different stages during the disaster, and analyzed the differences in people’s discussion content under different sentiments. We also analyzed the spatial pattern of sentiment and quantitatively explored the influencing factors of the sentiment spatial differences. The results showed that the overall public sentiment during Typhoon Haiyan tended to be positive, that compared with positive tweets, negative tweets contained more serious disaster information and more urgent demand information, and that the number of tweets, population, and the proportion of the young and middle-aged populations were the dominant factors in the sentiment spatial differences. View Full-Text
Keywords: natural disasters; sentiment analysis; topic classification; temporal and spatial evolution; factor detection; interactive detection natural disasters; sentiment analysis; topic classification; temporal and spatial evolution; factor detection; interactive detection
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MDPI and ACS Style

Zhang, T.; Cheng, C. Temporal and Spatial Evolution and Influencing Factors of Public Sentiment in Natural Disasters—A Case Study of Typhoon Haiyan. ISPRS Int. J. Geo-Inf. 2021, 10, 299. https://doi.org/10.3390/ijgi10050299

AMA Style

Zhang T, Cheng C. Temporal and Spatial Evolution and Influencing Factors of Public Sentiment in Natural Disasters—A Case Study of Typhoon Haiyan. ISPRS International Journal of Geo-Information. 2021; 10(5):299. https://doi.org/10.3390/ijgi10050299

Chicago/Turabian Style

Zhang, Ting, and Changxiu Cheng. 2021. "Temporal and Spatial Evolution and Influencing Factors of Public Sentiment in Natural Disasters—A Case Study of Typhoon Haiyan" ISPRS International Journal of Geo-Information 10, no. 5: 299. https://doi.org/10.3390/ijgi10050299

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