关键词: 新冠肺炎疫情, 微博评论, 情感分析, 主题聚类, 舆情演变, 社团网络, 时空数据分析, 网络爬虫

Abstract:

As a Public Health Emergency of International Concern (PHEIC), the COVID-19 pandemic caused great concern in social media all over the world. The content of Weibo comments is a collection of users' perceptions, attitudes, tendencies, and behaviors of the pandemic, and provides a high-timeliness and high-sequence text corpus for public opinion evolution research based on sentiment analysis. In this paper, we used a corpus obtained from People's Daily on Weibo during COVID-19 pandemic (January 23 - April 8, 2020) as our research data. First, we extracted emotional tendencies to classify text comments into positive and negative sentiments with SnowNLP, a Chinese natural language processing tool. Second, based on the Single-Pass clustering algorithm, we implemented text cluster analysis to explore hot topics about the pandemic situation. Moreover, we realized the information mining about public attention by using the Louvain community analysis algorithm. (1) On temporal dimension, the result of daily emotional trend analysis shows that the public has experienced three emotional phases, which are a period presenting anxiety and fear (January 23 - February 18), a period presenting steadiness and confidence (February 19 - March 15) and a period presenting tension and concern (March 16 - April 8). (2) On a spatial dimension, joint analysis of the number of users, the emotional states, and emotional projections among different provinces shows obvious differences in the public attention and emotional value of the COVID-19 pandemic. Additionally, for those Weibo users in COVID-19 affected areas, the level of their online participation is positively correlated with the pandemic severity and the value of the emotional state and emotional projection is lower. Meanwhile, those in worst-hit areas tend to have a higher impact on the evolution of public opinion. The results show that Weibo users in Guangdong Province and Heilongjiang Province have high levels of attention and low averages of emotional state and emotional projection. It can be judged the two provinces are still facing great pressure for pandemic prevention and control. Although Hubei Province is most affected by the pandemic, with a low emotional state value but a high emotional projection value, it is speculated Weibo users' comments on Hubei Province are more encouraging and praised. In addition, the number of confirmed cases in the northwestern region is relatively small, and the number of comment participation is less than in other regions, but the averages of emotional state and emotional projection are higher. The research applies natural language processing and network community detection algorithms to construct a methodological framework of public opinion analysis for social media comments. The developed framework has promising potentials, as it provides theoretical and practical support for related research on major public events.

Key words: COVID-19 pandemic, Sina Weibo comments, sentiment analysis, text clustering, evolution of public opinions, community network structure, temporal and spatial data analysis, web crawler