面向时序数据的多维度网络舆情演化分析研究OA

A multi-dimensional framework for analyzing the evolution of online public opinion was proposed,aiming to address the limitations in current research,such as a single perspective,incomplete topic mining,shallow sentiment analysis,and inaccurate identification of group behavior.Firstly,the four life cycle stages of public opinion evolution were divided based on social influence.Secondly,the topic evolution process was analyzed using the BERTopic model and similarity calculation.Sentiment fluctuations were studied by leveraging large language models.User groups were classified and opinion leaders were identified based on interaction relationships.Finally,the differences in social influence and sentiment across different periods and regions were visualized.Taking the event of''adjustment of stamp duty''in temporal public opinion data as an example,the study discovered that the topics of public concern,sentiment tendencies,user groups,and opinion leaders changed during the four life cycle stages of public opinion.The sentiment response in eastern regions was more positive,and the duration of public opinion topics and sentiments was longer.This study can provide technical support for revealing the laws of public opinion evolution and implementing effective public opinion monitoring.