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复杂系统与复杂性科学  2023, Vol. 20 Issue (3): 52-59    DOI: 10.13306/j.1672-3813.2023.03.007
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基于微博数据挖掘的贝叶斯观点演化模型
刘颖, 方爱丽, 魏新江
鲁东大学数学与统计科学学院,山东 烟台 264039
Bayesian Opinion Evolution Model Based on Weibo Data Mining
LIU Ying, FANG Aili, WEI Xinjiang
School of Mathematics and Statistics, Ludong University, Yantai 264039, China
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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。
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刘颖
方爱丽
魏新江
关键词 微博情感分析观点演化贝叶斯更新    
Abstract: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.
Key wordsweibo    sentiment analysis    opinions evolution    Bayesian updating
收稿日期: 2022-10-31      出版日期: 2023-10-08
ZTFLH:  O29  
基金资助:国家自然科学基金(62273172);山东省自然科学基金(ZR2020MF078)
通讯作者: 方爱丽(1971),女,山东蓬莱人,博士,副教授,主要研究方向为复杂网络理论与应用。   
作者简介: 刘颖(1998),女,山东蓬莱人,硕士研究生,主要研究方向为数据挖掘与分析。
引用本文:   
刘颖, 方爱丽, 魏新江. 基于微博数据挖掘的贝叶斯观点演化模型[J]. 复杂系统与复杂性科学, 2023, 20(3): 52-59.
LIU Ying, FANG Aili, WEI Xinjiang. Bayesian Opinion Evolution Model Based on Weibo Data Mining. Complex Systems and Complexity Science, 2023, 20(3): 52-59.
链接本文:  
https://fzkx.qdu.edu.cn/CN/10.13306/j.1672-3813.2023.03.007      或      https://fzkx.qdu.edu.cn/CN/Y2023/V20/I3/52
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