后疫情时代侨情危机状况识别方法

基于新冠疫情时代海外侨情危机事件频发,提出一种基于自动化信息要素抽取的新闻事件类型识别方法,对后疫情时代侨情新闻事件进行智能危机类别划分.首先,利用爬虫技术获取特定时间段的相关侨情事件新闻,进而采用信息抽取模型对语料数据进行信息要素抽取;然后,根据要素集的取值判断每条新闻的危机事件类型;最后,对2020年1月-8月的侨情新闻数据进行实证研究.结果表明:该方法不但能提升侨情分析的效率,还能进行多维度的危机状况信息可视化,有助于制定危机事件应对策略.

Method for Identifying Crisis Situation of Overseas Chinese in Post-Epidemic Era

Based on the frequent occurrence of overseas Chinese crisis events in the era of the COVID-19 epidemic, a method for identifying news event types based on automatic information element extraction is proposed, so as to intelligently classify overseas Chinese news events in the post-epidemic era. Firstly, the crawler technology is used to obtain relevant the news of overseas Chinese events in a specific time period, and then the information extraction model is used to extract the information elements from the corpus data. Then, the crisis event type of each news is judged according to the value of the element set. Finally, it makes an empirical analysis on the overseas Chinese news data from January to August 2020. The results show that this method can not only improve the efficiency of overseas Chinese situation analysis, but also visualize multi-dimensional crisis information, which is helpful to formulate crisis response strategies