摘要
【目的】提出基于语义的突发公共卫生事件网络舆情主题发现框架,发挥语义识别在舆情主题发现中的优势。【方法】从语法、语义、语用三个角度,利用依存句法分析、语义角色标注构建RDF三元组,分析语义图谱中的度及PageRank值判断核心节点,展开舆情主题发现研究并进行实证。【结果】实证研究中依据理论框架能够构建舆情主题语义图谱,可视化展现舆情主题间的语义关系,发现核心节点并聚焦于当事主体与官方政府两大主题。【局限】语义识别深度还有待提升。【结论】研究构建的理论模型在指导舆情主题语义发现方面具有一定的可行性,在舆情的主题发现上有一定的优势。
[Objective]This paper proposes a framework for identifying subjects of online opinion from public health emergencies,aiming to utilizing the advantages of semantic recognition.[Methods]First,we constructed RDF triples with dependency parsing analysis and semantic role annotations from the perspectives of grammar,semantics,and pragmatics.Then,we decided the core nodes based on degrees of the semantic graph and PageRank values.Finally,we conducted an empirical study to discover the subjects of public opinion.[Results]We successfully constructed a semantic graph for public opinion topics,and discovered the core nodes focusing on events and governments.[Limitations]The depth of semantic recognition needs to be improved.[Conclusions]The proposed model could help us identify public opinion topics.
作者
邵琦
牟冬梅
王萍
靳春妍
Shao Qi;Mu Dongmei;Wang Ping;Jin Chunyan(School of Public Health,Jilin University,Changchun 130021,China;The First Hospital of Jilin University,Changchun 130000,China)
出处
《数据分析与知识发现》
CSSCI
CSCD
北大核心
2020年第9期68-80,共13页
Data Analysis and Knowledge Discovery
基金
国家社会科学基金重大项目“大数据驱动的社交网络舆情主题图谱构建及调控策略研究”(项目编号:18ZDA310)的研究成果之一。
关键词
语义网络
舆情
主题发现
知识图谱
Semantic Network
Public Opinion
Subject Discovery
Knowledge Map
