Detection of Derived Public Opinion of Cyber Violence Incidents Based on BERTopic Model
[Research purpose]Timely detection and analysis of the derived public opinion of cyber violence incidents in the mass user-generated content can provide theoretical support for the evolution analysis of the chain of public opinion events,the intervention of similar public opinion events,and the monitoring judgment and early warning of derivative events.[Research method]The BERTopic model is used to model short text content topics and the underlying hierarchy of topics is shown in a clustering manner.Combining the advantages of word co-occurrence network in capturing information at the document-word level and the characteristics of Sankei chart to visually demonstrate the evolution process of public opinion,as well as designing the measurement algorithm of topic derivation degree according to the cosine similarity of word vector,while combining the advantages of word co-occurrence network in capturing information at the docu-ment-word level and the characteristics of Sankei chart to visually demonstrate the evolution process of public opinion,the impact and de-rivative relationships between topics are measured.[Research conclusion]In the control experiments of multiple sets of theme models un-der the open source dataset,the BERTopic model increased by 2.13％in the short text model and downstream task scores.In the applica-tion examples of the hotspot cyber violence,the methods of multi-dimensional fine-grained analysis and interactive visual exhibition could directly present the results of theme cluster,word meaning association and evolutionary situation,and accurately detect the derivative public opinion of cyber violence incidents.
network public opinioncyber violencederived public opinionpublic opinion monitoringshort texttopic modelingBERTopic model
万方数据
维普
