基于舆情客体与本体剥离的重大突发事件网络舆情本体演化强度研究

Research on Ontology Evolutionary Intensity of Network Public Opinions for Major Emergencies Based on the Separation of Public Opinion Object and Ontology

[目的/意义]重大突发事件网络舆情在传播过程中往往会出现不同的主题,而微博用户对不同主题的表达和关注也会直接影响网络舆情的传播速度和规模以及舆情事件的走向。针对重大突发事件的微博用户主题演化分析有助于应急管理部门更好地理解重大突发事件的发展轨迹以及公众在不同阶段的关注点,以便采取有效应对措施。[方法/过程]以网络舆情信息特征为立足点,辅以自然语言处理技术将舆情信息客体与本体进行剥离,结合重大突发事件特征,创新性提出以舆情客体信息为参照基线的舆情本体演化强度来反映微博主题演化趋势。[结果/结论]研究结果表明,面向网络舆情信息本体的主题分析,与网络舆情实际发展演化趋势更加贴近,对主题内容的揭示也更加全面。同时研究思路也对现有网络舆情主题分析的研究方法中单一求助于自然语言处理技术的优化与更新具有一定启示意义。

[Purpose/Significance]The communication process of network public opinions often has different themes in major emergencies.The expression and attention of Weibo users to different topics will also directly affect the propagation speed and scale of network public opinions and the trend of public opinion events.The theme evolution analysis of Weibo users for major emergencies helps emergency management departments better understand the development trajectory of major emergencies and the public’s concerns at different stages,so as to take effective countermeasures.[Method/Process]Based on the characteristics of Internet public opinion information,this paper used natural language processing technology to separate the object and ontology of public opinion information,and combining the characteristics of major emergencies,this paper creatively proposed that the evolution strength of public opinion ontology,which took the public opinion object information as the reference baseline,to reflect the evolution trend of microblog users’themes.[Result/Conclusion]The research results show that the theme analysis oriented to the Internet public opinion information ontology is closer to the actual development and evolution trend of Internet public opinion,and the disclosure of the theme contents is more comprehensive.At the same time,the research ideas of this paper also have some enlightenment significance for the optimization and update of existing research methods of online public opinion theme analysis that rely solely on natural language processing technology.