管理评论

管理评论 ›› 2021, Vol. 33 ›› Issue (10): 340-352.

• 案例研究 • 上一篇    

社交媒体数据对台风灾害的预警研究——以利奇马台风为例

郑苏晋1,2, 郭海若2, 宋姝凝2, 胡海涛2   

  1. 1. 中央财经大学中国精算研究院, 北京 100081;
    2. 中央财经大学保险学院, 北京 100081
  • 收稿日期:2020-11-20 出版日期:2021-10-28 发布日期:2021-11-29
  • 通讯作者: 郭海若(通讯作者),中央财经大学保险学院硕士研究生
  • 作者简介:郑苏晋,中央财经大学中国精算研究院、保险学院教授,硕士生导师,博士;宋姝凝,中央财经大学保险学院硕士研究生;胡海涛,中央财经大学保险学院硕士研究生。
  • 基金资助:
    中央财经大学保险学院科研创新基金（K2019001）。

Research on Early Warning of Typhoon Disasters Based on Social Media Data——A Case Study of Typhoon Lekima

Zheng Sujin1,2, Guo Hairuo2, Song Shuning2, Hu Haitao2   

  1. 1. China Institute for Actuarial Science, Central University of Finance and Economics, Beijing 100081;
    2. School of Insurance, Central University of Finance and Economics, Beijing 100081
  • Received:2020-11-20 Online:2021-10-28 Published:2021-11-29

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摘要： 巨灾事件的发生日益频繁，社交网络的即时性特点有助于灾情的迅速评估和灾后重建。本文以2019年9号台风“利奇马”为例，在新浪微博平台上收集了2019年8月9日—14日的260万条微博短文本数据，使用机器学习和优化情感词典两种方法分别对微博短文本进行分析。发现在相同的时间成本下，采用机器学习进行自然语言处理的效果远不如情感词典，机器学习的分析精度受主题与语料库的影响很大。在此基础上，本文利用台风主题下的情感词典对微博短文本进行情绪分析，发现灾损严重的省市情绪曲线会出现更大的波动，人身损失造成的情绪波动远大于财产损失造成的情绪波动。在浙江临海事件中，“好”的情绪曲线与“惧”的情绪曲线存在两个小时的时间差，表明“庆幸”的情绪达到峰值预示着未来很快可能有更重大的灾情发生，这其中的时间差为灾情预警提供了新的思路。

关键词: 社交媒体, 自然语言处理, 利奇马台风, 灾损预警

Abstract: Catastrophes are happening more and more frequently, and the timeliness of social networks takes an increasingly important role in the rapid assessment of disaster situation and post-disaster recovery. This paper conducts a case study of the typhoon Lichma that happened on August 9, 2019 and collects 2.6 million relevant short-text messages posted from August 9 to 14, 2019 on the Sina Weibo platform. This paper compares the results of sentiment analysis on Weibo short texts using the machine learning and the optimized sentiment dictionary. Firstly, this paper finds that under the same time cost, the effect of machine learning for natural language processing is far inferior to the sentiment dictionary. The analysis accuracy of machine learning is greatly affected by both the topic and the corpus. Then, this paper uses the sentiment dictionary based on typhoon to analyze the sentiment of the short-text of Weibo, and finds that the sentiment curves of the severely damaged provinces show greater fluctuations, and the emotional fluctuations caused by personal injuries are much greater than those caused by property losses. In the Linhai event, there was a two-hour time lag between the ‘good’ emotional curve and the ‘fear’ emotional curve, implying that the peak of the ‘rejoicing’ mood is a sign that there could be more serious damage to come soon, and the time lag provides a new idea for disaster early warning.

Key words: social media, natural language processing, typhoon Lekima, disaster warning