The aim of this study was to explore a method for developing an emotional evolution classification model for large-scale online public opinion of events such as Coronavirus Disease 2019 (COVID-19), in order to guide government departments to adopt differentiated forms of emergency management and to correctly guide online public opinion for severely afflicted areas such as Wuhan and those afflicted elsewhere in China. We propose the LDA-ARMA deep neural network for dynamic presentation and fine-grained categorization of a public opinion events. This was applied to a huge quantity of online public opinion texts in a complicated setting and integrated the proposed sentiment measurement algorithm. To begin, the Latent Dirichlet Allocation (LDA) was employed to extract information about the topic of comments. The autoregressive moving average model (ARMA) was then utilized to perform multidimensional sentiment analysis and evolution prediction on large-scale textual data related to COVID-19 published by netizens from Wuhan and other countries on Sina Weibo. The results show that Wuhan netizens paid more attention to the development of the situation, treatment measures, and policies related to COVID-19 than other issues, and were under greater emotional pressure, whereas netizens in the rest of the country paid more attention to the overall COVID-19 prevention and control, and were more positive and optimistic with the assistance of the government and NGOs. The average error in predicting public opinion sentiment was less than 5.64%, demonstrating that this approach may be effectively applied to the analysis of large-scale online public sentiment evolution.

本研究旨在探索一种针对 2019 冠状病毒病 (COVID-19) 等大型网络舆情事件的情绪演化分类模型开发方法，以指导政府部门采取差异化的应急管理形式。正确引导武汉等重灾区和全国其他受灾地区的网络舆论。我们提出了 LDA-ARMA 深度神经网络，用于动态呈现和细粒度的舆论事件分类。这被应用于复杂环境中的大量在线舆论文本，并集成了所提出的情感测量算法。首先，使用潜在狄利克雷分配 (LDA) 来提取有关评论主题的信息。然后利用自回归移动平均模型（ARMA）对来自武汉和其他国家的网友在新浪微博上发布的与 COVID-19 相关的大规模文本数据进行多维情感分析和演化预测。结果表明，武汉网民比其他问题更关注与COVID-19相关的局势、治疗措施和政策的发展，承受着更大的情绪压力，而全国其他地区的网民则更关注疫情。整体 COVID-19 预防和控制，在政府和非政府组织的帮助下更加积极和乐观。舆情预测平均误差小于5.64%，