Shaowen Wang

This paper applies the concept of symmetry to the design of a research methodology for public opinion evolution, emphasizing that both the construction and analysis processes of the method embody symmetrical principles. In today’s information age, dominated by social media, online platforms have become crucial venues for information dissemination. While the free flow of information promotes public participation, it also introduces certain challenges. Therefore, analyzing the evolution of public opinion and extracting public sentiment holds significant practical value for managing online public sentiment. This study takes the Zibo barbecue incident as a case study, utilizing the two-dimensional theory of emotion and Top2Vec for thematic analysis of public opinion comments. By combining sentiment dictionary methods with the RoBERTa model, we conduct a sentiment polarity analysis of public opinion comments. The results show that the RoBERTa model achieved an accuracy of 98.46% on the test set. The proposed method effectively uncovers public sentiment biases and the influencing factors on public emotions during the evolution of public opinion events, providing a more comprehensive understanding of the emotional dynamics throughout the development of public sentiment. This deeper insight aids in addressing issues related to public opinion more effectively.

In today’s information age, dominated by social media, online platforms have become crucial venues for information dissemination. While the free flow of information promotes public participation, it also introduces certain challenges. Therefore, analyzing the evolution of public opinion and extracting public sentiment holds significant practical value for managing online public sentiment. This study takes the Zibo Barbecue incident as a case study, utilizing the two-dimensional theory of emotion and Top2Vec for thematic analysis of public opinion comments. By combining sentiment dictionary methods with the RoBERTa model, we conducted a sentiment polarity analysis of public opinion comments. The results show that the RoBERTa model achieved an accuracy of 98.46% on the test set. The proposed method effectively uncovers public sentiment biases and the influencing factors on public emotions during the evolution of public opinion events, providing a more comprehensive understanding of the emotional dynamics throughout the development of public sentiment. This deeper insight aids in addressing issues related to public opinion more effectively.