Department of Science and Technology, West China Hospital, Sichuan University, Chengdu, Sichuan, China.
West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan, China.
Int J Public Health. 2023 Aug 10;68:1606074. doi: 10.3389/ijph.2023.1606074. eCollection 2023.
In December 2022, the Chinese government announced the further optimization of the implementation of the prevention and control measures of COVID-19. We aimed to assess internet-using public expression and sentiment toward COVID-19 in the relaxation of control measures in China. We used a user-simulation-like web crawler to collect raw data from Sina-Weibo and then processed the raw data, including the removal of punctuation, stop words, and text segmentation. After performing the above processes, we analyzed the data in two aspects. Firstly, we used the Latent Dirichlet Allocation (LDA) model to analyze the text data and extract the theme. After that, we used sentiment analysis to reveal the sentiment trend and the geographical spatial sentiment distribution. A total of five topics were extracted according to the LDA model, namely, Complete liberalization, Resource supply, Symptom, Knowledge, and Emotional Outlet. Furthermore, sentiment analysis indicates that while the percentages of positive and negative microblogs fluctuate over time, the overall quantity of positive microblogs exceeds that of negative ones. Meanwhile, the geographical dispersion of public sentiment on internet usage exhibits significant regional variations and is subject to multifarious factors such as economic conditions and demographic characteristics. In the face of the relaxation of COVID-19 control measures, although concerns arise among people, they continue to encourage and support each other.
2022 年 12 月,中国政府宣布进一步优化实施 COVID-19 防控措施。本研究旨在评估中国放松防控措施后,互联网用户对 COVID-19 的表达和情绪。我们使用用户模拟式网络爬虫从新浪微博上收集原始数据,然后对原始数据进行处理,包括去除标点符号、停用词和文本分段。完成上述步骤后,我们从两个方面分析数据。首先,我们使用潜在狄利克雷分配(LDA)模型分析文本数据并提取主题。然后,我们使用情感分析揭示情感趋势和地理空间情感分布。根据 LDA 模型共提取了五个主题,分别是完全自由化、资源供应、症状、知识和情感出口。此外,情感分析表明,虽然积极和消极微博的百分比随时间波动,但积极微博的总量超过消极微博。同时,互联网使用中的公众情绪的地理分布表现出显著的区域差异,并受到经济条件和人口特征等多种因素的影响。面对 COVID-19 防控措施的放松,尽管人们感到担忧,但他们继续相互鼓励和支持。