【目的】提出基于語義的突發公共衛生事件網絡輿情主題發現框架,發揮語義識別在輿情主題發現中的優勢。【方法】從語法、語義、語用三個角度,利用依存句法分析、語義角色標注構建RDF三元組,分析語義圖譜中的度及PageRank值判斷核心節點,展開輿情主題發現研究并進行實證。【結果】實證研究中依據理論框架能夠構建輿情主題語義圖譜,可視化展現輿情主題間的語義關系,發現核心節點并聚焦于當事主體與官方政府兩大主題。【局限】語義識別深度還有待提升。【結論】研究構建的理論模型在指導輿情主題語義發現方面具有一定的可行性,在輿情的主題發現上有一定的優勢。

[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.