基于深度学习的地震舆情信息提取及时空可视化

Seismic public opinion information extraction and temporal-spatial visualization based on deep learning

社交媒体平台作为网络舆情的重要载体,可为地震等重大灾害事件的应急管理提供及时有效的信息,但针对社交媒体数据中蕴含的细粒度主题信息提取并分析灾情的研究尚有欠缺。基于深度学习算法提取社交媒体数据中蕴含的细粒度地震主题信息,以2021年5月21日“大理漾濞6.4级地震”事件为例,构建一个主题信息提取流程框架,通过对模型进行优化训练,整体精度达到80%以上,将新获取的青海地震数据集输入模型进行验证,也达到预期效果,2次地震验证结果说明该模型在灾害主题信息提取中具有可行性。最后,结合关键词挖掘技术、情感分析和核密度估计等辅助舆情分析法将地震主题时空可视化,为灾情研判和应急管理工作提供新思路。

As an important carrier of network public opinion,social media platforms can provide timely and effective information for emergency management of major disaster events such as earthquakes.However,there is still a lack of research on extracting and analyzing the disaster situation with fine-grained topic information contained in social media data.This paper extracts fine-grained seismic topic information contained in social media data based on deep learning algorithms.Take the“Dali Yangbi M s6.4 earthquake”event on May 21,2021 as an example,build a framework of topic information extraction process,by optimizing the training of the model,the overall accuracy reaches more than 80%.The newly acquired Qinghai seismic data set was input into the model for verification,and the expected effect was also achieved.The verification results of two earthquakes show that the model is feasible in the extraction of disaster theme information.Finally,combined with the auxiliary public opinion analysis methods such as keyword mining technology,sentiment analysis and kernel density estimation,the earthquake theme was visualized in space and time.It provides new ideas for disaster research and judgment and emergency management.