%0 Journal Article
%T 基于机器学习的新型冠状肺炎的舆情分析<br>Public Opinion Analysis of Novel Coronavirus Pneumonia Based on Machine Learning
%A 谢婷
%A 罗清
%J Hans Journal of Data Mining
%P 114-122
%@ 2163-1468
%D 2022
%I Hans Publishing
%R 10.12677/HJDM.2022.122013
%X 新冠疫情的爆发和肆虐引起群众关注，互联网上的相关话题不断攀升。如何利用计算机方法和数据分析算法准确地识别热点新闻和疫情主题，挖掘民众关注的话题，分析舆论走势，显得至关重要。本文提出一种基于GSDMM主题挖掘的“新冠肺炎疫情”舆情分析方法，利用数据预处理、特征提取、词云可视化技术挖掘目标数据的热点主题，再采用GSDMM主题模型、聚类分析对目标数据进行分析挖掘。通过深入进行了面向人民网的GSDMM短文本聚类算法研究，得到大家都一直十分关心中国和世界的疫情形势和经济形势的信息。此次肺炎疫情热点主题包括疫情、防控、工作、肺炎、患者等。<br />
The outbreak and ravages of the new crown epidemic have aroused the attention of the masses, and related topics on the Internet have continued to rise. How to use computer methods and data analy-sis algorithms to accurately identify hot news and epidemic topics, dig out topics of public concern, and analyze the trend of public opinion is of great importance. In this paper, a “new crown pneumo-nia epidemic” public opinion analysis method based on GSDMM theme mining is proposed, which uses data preprocessing, feature extraction, and word cloud visualization technology to mine the hot topics of target data, and then uses GSDMM theme model and cluster analysis to analyze and mine target data. Through the in-depth research of the GSDMM short text clustering algorithm for the People’s Network, we have obtained information that everyone has always been very concerned about the epidemic situation and economic situation in China and the world. The hot topics of the pneumonia epidemic include epidemic situation, prevention and control, work, pneumonia, patients, etc.
%K GSDMM，主题模型，新冠疫情，聚类算法<br>GSDMM
%K Topic Model
%K New Crown Epidemic
%K Clustering Algorithm
%U http://www.hanspub.org/journal/PaperInformation.aspx?PaperID=50031