关键词: 新冠肺炎, 微博, 情感分析, 空间聚类, 舆情, 提取, 热点挖掘, 爬虫

Abstract:

Since December 2019, COVID-19 has rapidly swept the world. As of May 10, 2020, 16:40 PM, Beijing time, the global confirmed COVID-19 cases reached 4,115,662, which has become a major global issue. Social media platforms such as microblog have become the important channel for information transmission and an effective sensor of public sentiment. In-depth mining and analysis of microblog information can not only characterize the public opinion, but also help the government to conduct targeted guidance on public sentiment and properly control public opinion. Therefore, this study collected more than 330,000 Sina Weibo data about COVID-19 from January 18, 2020 to January 28, 2020. Based on the spatial clustering method using Louvain and K-means and an improved BTM subject word extraction algorithm, users' attention information and emotional characteristics are labeled with their locations. Thus, the evaluation method of public opinion is constructed by integrating user's location information, which is able to analyze the characteristics of public opinion and the difference in the topics concerned at different regions. Our results show that the characteristics of public opinion in different regions can be comprehensively evaluated using the spatial clustering method based on Louwain and K-mean. The BTM subject word extraction method based on BERT word vector can effectively make up the disadvantages of traditional subject word extraction methods that need large computation and have data redundancy, and thus has stronger expression ability in user data mining. The hot topics concerned in different regions have certain differences. The public opinion analysis method proposed in this paper can effectively reflect the public opinion characteristics of different regions and provide reference for the public opinion analysis of major public health events.

Key words: COVID-19, Weibo, sentiment analysis, spatial clustering, public opinion, subject word extraction, hot mining, the crawler