[Objective] This paper provides a new model to analyze public opinion from the perspective of sentiment divergence, aiming to address online public opinion events effectively. [Methods] First, we introduced the concept of sentiment disagreement and proposed a multi-level sentiment disagreement algorithm. Then, we constructed a multi-level sentiment disagreement analysis model for online opinion events. This model could calculate sentiment values and disagreement for the online opinion event, comment object, and user layers to perform correlation analysis among the three layers. [Results] Introducing sentiment disagreement can compensate for the lack of research on netizens’ opinion divergence in the original sentiment analysis. This model can identify the critical nodes of public opinion events and the comments generating significant controversy. It also evaluates the effectiveness of public opinion guidance and locates the causes of controversies. [Limitations] We only retrieved the needed data from Sina Weibo (Microblog). More research is needed to collect data from social platforms like Douban and Zhihu. [Conclusions] The proposed model can be applied to monitor the key nodes of public opinion events, select different public opinion guidance methods based on the reasons for controversies, and evaluate the effectiveness of public opinion guidance.
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