[1] 康斌. 我国台风灾害统计分析[J]. 中国防汛抗旱, 2016, 26(2):36-40. KANG B. Statistical analysis of typhoon disasters in China[J]. Flood Control and Drought Relief in China, 2016, 26(2):36-40. (in Chinese)
[2] 中国气象局. 中国气象灾害年鉴2019[M]. 北京:气象出版社, 2019. China Meteorological Administration. China meteorological disaster yearbook 2019[M]. Beijing:Meteorological Press, 2019. (in Chinese)
[3] 中国气象局. 中国气象灾害年鉴2018[M]. 北京:气象出版社, 2018. China Meteorological Administration. China meteorological disaster yearbook 2018[M]. Beijing:Meteorological Press, 2018. (in Chinese)
[4] 中国气象局. 中国气象灾害年鉴2017[M]. 北京:气象出版社, 2017. China Meteorological Administration. China meteorological disaster yearbook 2017[M]. Beijing:Meteorological Press, 2017. (in Chinese)
[5] 中国气象局. 中国气象灾害年鉴2016[M]. 北京:气象出版社, 2016. China Meteorological Administration. China meteorological disaster yearbook 2016[M]. Beijing:Meteorological Press, 2016. (in Chinese)
[6] 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.
[7] 陈梓, 高涛, 罗年学, 等.反映自然灾害时空分布的社交媒体有效性探讨[J].测绘科学, 2017, 42(8):44-48. 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)
[8] 白华, 林勋国.基于中文短文本分类的社交媒体灾害事件检测系统研究[J].灾害学, 2016, 31(2):19-23. 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)
[9] 彭敏, 官宸宇, 朱佳晖, 等.面向社交媒体文本的话题检测与追踪技术研究综述[J] 武汉大学学报·理学版, 2016, 62(3):197-217. 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)
[10] 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.
[11] 刘宏波, 翟国方.基于社交媒体信息不同灾害的社会响应特征比较研究[J].灾害学, 2017, 32(1):187-193. 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)
[12] 刘超然.在线新闻网民评论情感倾向性分析及可视化研究[D]. 哈尔滨:哈尔滨工业大学, 2018. LIU C R. Online news netizens comment on emotional orientation analysis and visualization[D]. Harbin:Harbin Institute of Technology, 2018. (in Chinese)
[13] 仇培元, 陆锋, 张恒才, 等. 蕴含地理事件微博客消息的自动识别方法[J].地球信息科学学报, 2016, 18(7):886-893. 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)
[14] 杨腾飞, 解吉波, 李振宇, 等. 微博中蕴含台风灾害损失信息识别和分类方法[J]. 地理信息科学学报, 2018, 20(7):906-917. 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)
[15] SAKAKI T, OKAZAKI M, MATSUO Y. Earthquake shakes Twitter users:Real-time event detection by social sensors[C]//Proceedings of the 19th International Conference on World Wide Web. New York, USA:ACM, 2010:851-860.
[16] 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.
[17] ALFARRARJEH A, AGRAWAL S, KIM S H, et al. Geo-spatial multimedia sentiment in disasters[C]//The 4th IEEE International Conference on Data Science and Advanced Analytics 2017. Tokyo, Japan:IEEE, 2017.
[18] 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.
[19] CHIO S, BAE B. The real-time monitoring system of social big data for disaster management[M]. Berlin:Springer, 2015.
[20] PETER D.T. Learning algorithms for keyphrase extraction[J]. Information Retrieval, 2000, 2(4):303-336.
[21] YANG T, XIE J, LI G. A social media based dataset of typhoon disasters[DB]. Science Data Bank, 2017, DOI:10.11922/sciencedb.547.
[22] SINGH J, SINGH G, SINGH R, et al. Optimizing accuracy of sentiment analysis using deep learning based classification technique[C]//International Conference on Recent Developments in Science, Engineering and Technology. Singapore:Springer, 2017:516-532.
[23] PAOLA V, GIOVANNI S, ALBERTO E. et al. Twitter mining for fine-grained syndromic surveillance[J]. Artificial Intelligence in Medicine, 2014, 61(3):153-163.
[24] KRYVASHEYEU Y, CHEN H H, OBRADOVICH N, et al. Rapid assessment of disaster damage using social media activity[J]. Science Advances, 2016, 2(3):e1500779.
[25] 李志娟. 台风灾害损失预测方法研究[D].广州:华南理工大学, 2016. LI Z J. Research on Forecast Method of Typhoon Disaster Loss[D]. Guangzhou:South China University of Technology, 2016. (in Chinese)
[26] VEGA S H, ELHORST J P. The SLX model[J]. Journal of Regional Science, 2015, 55(3):339-363.
[27] WU D S, CUI Y W. Disaster early warning and damage assessment analysis using social media data and geo-location information[J]. Decision Support Systems, 2018(111):48-59.
[28] MEIJERS E, BURGER M. Stretching the concept of borrowed size[J]. Urban studies, 2017, 54(1):269-291.