| 基于变分推理的网络舆情传播模式分类 |
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| 引用本文： | 唐红梅,唐文忠,李瑞晨,王衍洋,王丽宏. 基于变分推理的网络舆情传播模式分类[J]. 北京航空航天大学学报, 2022, 48(2): 209-216. DOI: 10.13700/j.bh.1001-5965.2020.0538 |
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| 作者姓名： | 唐红梅 唐文忠 李瑞晨 王衍洋 王丽宏 |
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| 作者单位： | 1.北京航空航天大学 计算机学院, 北京 100083 |
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| 基金项目： | 新疆维吾尔自治区自然科学基金（2020D01A95）~~； |
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| 摘 要： | 随着网络社交媒体的快速发展，对舆情信息的传播模式进行分析成为研究热点。针对网络舆情传播模式分类任务中，小样本数据多路径生成分类正确率低的问题，提出了舆情传播领域知识图谱结构定义，建立了基于微博数据的舆情传播知识图谱与舆情传播分析任务数据集，使用GraphDIVA模型进行舆情传播模式分类，并在自建数据集中进行了舆情传播模式分类25样本测试实验。结果表明:模型在经过20轮训练后，分类正确率从76%提升到89.4%，说明GraphDIVA模型在减少训练次数、提升分类正确率方面具有更优的效果。
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| 关 键 词： | 舆情传播模式 知识图谱 知识图谱推理 图神经网络 模式分析 |
| 收稿时间： | 2020-09-22 |
Classification of network public opinion propagation pattern based on variational reasoning |
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| TANG Hongmei,TANG Wenzhong,LI Ruichen,WANG Yanyang,WANG Lihong. Classification of network public opinion propagation pattern based on variational reasoning[J]. Journal of Beijing University of Aeronautics and Astronautics, 2022, 48(2): 209-216. DOI: 10.13700/j.bh.1001-5965.2020.0538 |
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| Authors: | TANG Hongmei TANG Wenzhong LI Ruichen WANG Yanyang WANG Lihong |
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| Affiliation: | 1.School of Computer Science and Engineering, Beihang University, Beijing 100083, China2.Science and Technology Projects Service Center of Xinjiang Uygur Autonomous Region, Urumqi 830000, China3.School of Aeronautic Science and Engineering, Beihang University, Beijing 100083, China4.Beihang Jiangxi Research Institute, Nanchang 330096, China5.National Computer Network Emergency Response Technical Team/Coordination Center of China, Beijing 100029, China |
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| Abstract: | With the rapid development of online social media, the analysis of the dissemination mode of public opinion information has become a research hotspot.Aiming at the problem of low classification accuracy of small sample data multi-path generation in the classification task of the network public opinion spreading pattern, the definition of the knowledge graph structure in the field of public opinion dissemination is proposed, builds a public opinion dissemination knowledge graph and public opinion dissemination analysis task data set based on Weibo data, uses the GraphDIVA model to classify public opinion propagation patterns, and conducts a 25-sample test experiment of public opinion propagation pattern classification in the self-built data set. The results show that, after 20 rounds of training, the classification accuracy rate of the model has increased from 76% to 89.4%. It can be seen that the GraphDIVA model has a better effect in reducing the number of training and improving the classification accuracy rate. |
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