微博环境下新冠肺炎疫情事件对网民情绪的影响分析

Research on the Influence of COVID-19 Event on the Netizen Emotion under the Microblog Environment

[目的/意义]微博作为一种重要的信息传播载体,在疫情信息发布与传播中发挥着重要作用。深入分析疫情信息中蕴含的疫情事件及其对网民情绪的影响,有助于各级政府准确掌握网络舆论情况,科学高效地做好防控宣传和舆情引导工作。[方法/过程]以新冠肺炎疫情相关的微博新闻及其评论作为研究对象,利用条件随机场(Conditional Random Field,CRF)模型从微博新闻中抽取疫情事件并建立疫情事件画像;在情感词典的基础上,引入双向长短期记忆网络(Bidirectional Long Short-Term Memory,Bi-LSTM)模型建立网民情绪画像;利用基于自注意力机制的Bi-LSTM模型对疫情事件与网民情绪进行关联分析。[结果/结论]真实语料集上的实验结果表明,围绕捐资、防控、临床和英雄等主题,CRF模型疫情事件抽取的F值均达到73%以上,Bi-LSTM模型网民情绪识别的F值均在70%以上,基于注意力机制的Bi-LSTM模型给出的网民情绪分布基本符合疫情发展态势。

[Purpose/Significance]Since the outbreak of COVID-19,microblog,as an important carrier of information transmission,has played an indispensable role in the release and dissemination of epidemic information.The in-depth analysis of the epidemic information and the impact on the netizen emotion contained in COVID-19 event can help governments grasp the public opinions on the Internet as well as do a good work in epidemic prevention and public opinion guidance efficiently.[Method/Process]Taking the microblog news and its comments related to the COVID-19 as the research object,this paper then uses Conditional Random Field(CRF)model to extract event information and establish an event portrait for the COVID-19,introduces the Bidirectional Long Short-Term Memory(Bi-LSTM)model to build netizen emotional portrait based on sentiment dictionary,and finally analyzes the relationship between epidemic events and netizen emotions by using an attention-based Bi-LSTM model.[Result/Conclusion]The experimental results on the real corpus with the topics of material,prevention and control,clinical and hero show that the F value of the CRF and Bi-LSTM model has reached more than 73% and 70% respectively.The distribution of netizen emotions is basically in line with the epidemic development based on the attention-based Bi-LSTM model.