社交媒体的情感挖掘在服务减灾中的应用
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摘要
社交媒体因其广泛的公众参与性和多源信息的快速传播性已成为灾情信息获取的重要途径,在近年来的灾害应急救援中发挥着重要的作用。我国是一个风灾频发的国家,有效的管理和利用社交媒体数据辅助减灾救援有着重要的理论和现实意义。然而目前,国内面向微博文本理解和情感分析在减轻灾害方面的研究还十分稀缺。针对目前研究的不足,本文以中文社交媒体为研究对象,通过机器学习的方法挖掘风灾期间的公众情感变化,并结合GIS空间分析技术对灾情的发展与影响进行刻画,最后以2017年台风"天鸽"登陆珠海市为案例证明方法的可行性。
Social media has become an important way to obtain disaster information because of its extensive public participation and rapid dissemination of multi-source information.It plays an important role in disaster emergency rescue in recent years.China is a country with frequent windstorms.Effective management and use of social media data to assist disaster reduction and rescue has important theoretical and practical significance.However,at present,domestic research on microblogging text understanding and sentiment analysis in disaster mitigation is still very scarce.In view of the shortcomings of the current research,this paper takes Chinese social media as the research object,and uses the machine learning method to mine the public emotional changes during the storm,and combines the GIS spatial analysis technology to describe the development and impact of the disaster.Finally,the 2017 typhoon Hato landed in Zhuhai City as a case to prove the feasibility of the method.
Social media has become an important way to obtain disaster information because of its extensive public participation and rapid dissemination of multi-source information.It plays an important role in disaster emergency rescue in recent years.China is a country with frequent windstorms.Effective management and use of social media data to assist disaster reduction and rescue has important theoretical and practical significance.However,at present,domestic research on microblogging text understanding and sentiment analysis in disaster mitigation is still very scarce.In view of the shortcomings of the current research,this paper takes Chinese social media as the research object,and uses the machine learning method to mine the public emotional changes during the storm,and combines the GIS spatial analysis technology to describe the development and impact of the disaster.Finally,the 2017 typhoon Hato landed in Zhuhai City as a case to prove the feasibility of the method.
引文
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[6]王艳东,李昊,王腾,等.基于社交媒体的突发事件应急信息挖掘与分析[J].武汉大学学报(信息科学版),2016,41(3):290-297.
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[9]CHAE J,THOM D,JANG Y,et al.Public Behavior Response Analysis in Disaster Events Utilizing Visual Analytics of Microblog data[J].Computers&Graphics,2014,38(1):51-60.
[10]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.
[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.
[12]冯冬宁,薛重生,张兴福,等.基于GIS的滑坡灾害应用研究[J].北京测绘,2008(3):20-22.
[13]杨亮,邹逸江.基于GIS的沿海农房防台减灾预警初步框架研究[J].北京测绘,2009(3):13-17.
[14]BURBIDGE R,TROTTER M,BUXTON B,et al.Drug Design by Machine Learning:Support Vector Machines for Pharmaceutical Data Analysis[J].Computers&Chemistry,2002,26(1):5-14.
[15]YANG Y,PEDERSEN J O.A Comparative Study on Feature Selection in Text Categorization[C]∥Proc.International Conference on Machine Learning,1997:412-420.
[16]JONES K S.A Statistical Interpretation of Term Specificity and Its Application in Retrieval[J].Journal of Documentation,1972,28(1):11-21.
[2]American Red Cross.More Americans using mobile apps in emergencies[EB/OL].(2012-08_31)[2013-04-10].http://www.redcross.org/news/pressrelease/More-Americans-U-sing-Mobile-Apps-in-Emergencies.
[3]牟乃夏,张恒才,陈洁,等.轨迹数据挖掘城市应用研究综述[J].地球信息科学学报,2015,17(10):1136-1142.
[4]SAKAKI T,OKAZAKI M,MATSUO Y.Tweet Analysis for Real-Time Event Detection and Earthquake Reporting System Development[J].IEEE Transactions on Knowledge&Data Engineering,2013,25(4):919-931.
[5]BIRD D,LING M,HAYNES K.Flooding Facebook-the Use of Social Media during the Queensland and Victorian Floods[J].Australian Journal of Emergency Management,2012,27(1):27-33.
[6]王艳东,李昊,王腾,等.基于社交媒体的突发事件应急信息挖掘与分析[J].武汉大学学报(信息科学版),2016,41(3):290-297.
[7]SAKAKI T,OKAZAKI M,MATSUO Y.Earthquake Shakes Twitter Users:Real-time Event Detection by Social Sensors[C]∥International Conference on World Wide Web.ACM,2010:851-860.
[8]QU Y,HUANG C,ZHANG P,et al.Microblogging After a Major Disaster in China:A Case Study of the 2010Yushu Earthquake[C]∥ACM Conference on Computer Supported Cooperative Work,CSCW 2011,Hangzhou,China,March.DBLP,2011:25-34.
[9]CHAE J,THOM D,JANG Y,et al.Public Behavior Response Analysis in Disaster Events Utilizing Visual Analytics of Microblog data[J].Computers&Graphics,2014,38(1):51-60.
[10]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.
[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.
[12]冯冬宁,薛重生,张兴福,等.基于GIS的滑坡灾害应用研究[J].北京测绘,2008(3):20-22.
[13]杨亮,邹逸江.基于GIS的沿海农房防台减灾预警初步框架研究[J].北京测绘,2009(3):13-17.
[14]BURBIDGE R,TROTTER M,BUXTON B,et al.Drug Design by Machine Learning:Support Vector Machines for Pharmaceutical Data Analysis[J].Computers&Chemistry,2002,26(1):5-14.
[15]YANG Y,PEDERSEN J O.A Comparative Study on Feature Selection in Text Categorization[C]∥Proc.International Conference on Machine Learning,1997:412-420.
[16]JONES K S.A Statistical Interpretation of Term Specificity and Its Application in Retrieval[J].Journal of Documentation,1972,28(1):11-21.