摘要
考虑到在微博信息传播过程中,每一位网络用户的观点都受到其前一位网络用户观点的影响,提出建立基于微博数据挖掘的贝叶斯观点演化模型。以“动态清零政策是我国抗疫总方针”为关键话题词,利用Python爬取微博评论数据,经过数据预处理和分词,对贝叶斯观点演化模型进行实证分析,结果发现官方媒体对舆情的及时引导对情感演化倾向起到重要作用。
Considering that in the process of microblog information dissemination,the opinion of each network user is influenced by the opinions of the previous network user.Therefore,we propose a Bayesian opinion evolution model based on weibo data mining.With“dynamic zero policy is the general policy of China′s fight against the epidemic”as the key word,Python is used to crawl the Weibo comment data.After data preprocessing and word segmentation,the Bayesian opinion evolution model is empirically analyzed.Empirical analysis shows that the timely guidance of public opinion by official media plays an important role in sentiment evolution tendency.
作者
刘颖
方爱丽
魏新江
Mining LIU Ying;FANG Aili;WEI Xinjiang(School of Mathematics and Statistics,Ludong University,Yantai 264039,China)
出处
《复杂系统与复杂性科学》
CAS
CSCD
北大核心
2023年第3期52-59,共8页
Complex Systems and Complexity Science
基金
国家自然科学基金(62273172)
山东省自然科学基金(ZR2020MF078)。
关键词
微博
情感分析
观点演化
贝叶斯更新
weibo
sentiment analysis
opinions evolution
Bayesian updating
