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This study searched Microblog data related to Typhoon "Mangkhut" in 2018 and "Lekima" in 2019 and then used the Bayesian sentiment analysis model to analyze the public opinions related to these typhoons. The results show two different typhoon disaster public opinion temporal and spatial evolution laws and an emotional evolution law. Then, the urban typhoon disaster loss was estimated for coastal and inland cities based on the temporal and spatial evolution and emotional evolution laws. The data includes the city's geographic location, economy, population and typhoon disaster damage as well as sentiments. The disaster damage assessment model is consistent with the disaster assessment. The research results and methods provide references for disaster research and disaster relief demand analyses and guidelines for supply allocation in cities during initial emergency responses during typhoons.
KANG B. Statistical analysis of typhoon disasters in China[J]. Flood Control and Drought Relief in China, 2016, 26(2): 36-40. (in Chinese)
China Meteorological Administration. China meteorological disaster yearbook 2019[M]. Beijing: Meteorological Press, 2019. (in Chinese)
China Meteorological Administration. China meteorological disaster yearbook 2018[M]. Beijing: Meteorological Press, 2018. (in Chinese)
China Meteorological Administration. China meteorological disaster yearbook 2017[M]. Beijing: Meteorological Press, 2017. (in Chinese)
China Meteorological Administration. China meteorological disaster yearbook 2016[M]. Beijing: Meteorological Press, 2016. (in Chinese)
CHAE J, THOM D, JANG Y, et al. Special section on visual analytics: Public behavior response analysis in disaster events utilizing visual analytics of microblog data[J]. Computers & Graphics, 2014, 38(1): 51-60.
CHEN Z, GAO T, LUO N X, et al. Social media effectiveness to reflect the spatial and temporal distribution of natural disasters[J]. Science of Surveying and Mapping, 2017, 42(8): 44-48. (in Chinese)
BAI H, LIN X G. Social media disaster event detection system based on Chinese short text classification[J]. Journal of Catastrophology, 2016, 31(2): 19-23. (in Chinese)
PENG M, GUAN C Y, ZHU J H, et al. A survey of topic detection and tracking technology for social media texts[J]. Journal of Wuhan University (Science Edition), 2016, 62(3): 197-217. (in Chinese)
MARK A S, ALISA E P, et al. Making the most of a brave new world: Opportunities and considerations for using Twitter as a public health monitoring tool[J]. Preventive Medicine, 2014, 63(6): 109-111.
LIU H B, ZHAI G F. A comparative study of the social response characteristics of different disasters based on social media information[J]. Journal of Catastrophology, 2017, 32(1): 187-193. (in Chinese)
QIU P Y, LU F, ZHANG H C, at al. Containing automatic recognition methods for geo-event micro-blog messages[J]. Journal of Geo-information Science, 2016, 18(7): 886-893. (in Chinese)
YANG T F, JIE J B, LI Z Y, et al. Identification and classification of typhoon disaster loss information in Weibo[J]. Journal of Earth Sciences, 2018, 20(7): 906-917. (in Chinese)
KUMAR M A, GOPAL M. A comparison study on multiple binary-class SVM methods for unilabel text categorization[J]. Pattern Recognition Letters, 2010, 31(11): 1437-1444.
XU B, GUO X, YE Y, et al. An improved random forest classifier for text categorization[J]. Journal of Computers, 2012, 7(12): 2913-2920.
CHIO S, BAE B. The real-time monitoring system of social big data for disaster management[M]. Berlin: Springer, 2015.
PETER D.T. Learning algorithms for keyphrase extraction[J]. Information Retrieval, 2000, 2(4): 303-336.
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