{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,9]],"date-time":"2026-04-09T16:24:49Z","timestamp":1775751889409,"version":"3.50.1"},"reference-count":56,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2021,5,5]],"date-time":"2021-05-05T00:00:00Z","timestamp":1620172800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Key Research and Development Plan of China","award":["No.2017YFB0504102"],"award-info":[{"award-number":["No.2017YFB0504102"]}]},{"name":"National Natural Science Foundation of China","award":["No. 41771537"],"award-info":[{"award-number":["No. 41771537"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>The public\u2019s 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\u2019s 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\u2019s 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.<\/jats:p>","DOI":"10.3390\/ijgi10050299","type":"journal-article","created":{"date-parts":[[2021,5,5]],"date-time":"2021-05-05T11:06:01Z","timestamp":1620212761000},"page":"299","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":35,"title":["Temporal and Spatial Evolution and Influencing Factors of Public Sentiment in Natural Disasters\u2014A Case Study of Typhoon Haiyan"],"prefix":"10.3390","volume":"10","author":[{"given":"Ting","family":"Zhang","sequence":"first","affiliation":[{"name":"Key Laboratory of Environmental Change and Natural Disaster, Beijing Normal University, Beijing 100875, China"},{"name":"State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, Beijing 100875, China"},{"name":"Faculty of Geographical Science, Center for Geodata and Analysis, Beijing Normal University, Beijing 100875, China"}]},{"given":"Changxiu","family":"Cheng","sequence":"additional","affiliation":[{"name":"Key Laboratory of Environmental Change and Natural Disaster, Beijing Normal University, Beijing 100875, China"},{"name":"State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, Beijing 100875, China"},{"name":"Faculty of Geographical Science, Center for Geodata and Analysis, Beijing Normal University, Beijing 100875, China"},{"name":"National Tibetan Plateau Data Center, Beijing 100101, China"}]}],"member":"1968","published-online":{"date-parts":[[2021,5,5]]},"reference":[{"key":"ref_1","first-page":"82","article-title":"Analysis of public opinion heats of emergencies based on response level","volume":"22","author":"Cao","year":"2014","journal-title":"China Manag. 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