# Quantification and Analysis of Group Sentiment in Electromagnetic Radiation Public Opinion Events
## 4 Research Design and Plan
### Analysis of Research Result  
This study analyzed Weibo posts containing the keyword \"electromagnetic radiation\" along with user behavior data from 25 October 2021 to 10 September 2022. After cleaning the data, we quantified collective emotions and analyzed accounts for 222 Weibo posts,222 Weibo posters, 2604 first-level comments, 1839 first-level commenters, 1809 second-level comments, and 425 second-level commenters. Considering how user interactions and behaviors influence the spread of emotions, this study created macro- and microclusters within defined emotion propagation chains. We quantified local emotion values by calculating cluster density, cluster trustworthiness, and the average emotional tendency of clusters. Finally, we used a weighting method to evaluate the global collective emotions triggered by the posts. Our analysis showed that among the 222 Weibo posts, 158 had positive comprehensive emotion values, 9 showed no emotion value, and 55 had negative comprehensive emotion values. The mean sentiment score across 222 posts was 0.019 (SD = 0.082), with a 95% confidence interval of [0.005, 0.033], indicating a generally neutral-to-positive trend, but with large variance. To further explain this trend, we plotted the top five posts with the highest positive and negative emotional values (Figure 2), with negative emotions displayed as absolute values. The highest absolute value for negative emotion was 0.30, while the lowest positive emotion value was 0.32, which is higher than the maximum absolute value for negative emotion. This means that the top five negative emotion values were all smaller than the positive emotion values. Thus, the overall emotion for this topic on Weibo is positive. The results suggest that discussions about EMR have not led to widespread negative emotions despite public health concerns. The following analysis will examine the characteristics of public opinion related to EMR from the perspectives of public opinion monitoring, public opinion analysis, and public opinion response, and will propose comprehensive and effective strategies for managing network public opinion based on the experimental results. Figure 2: Positive and negative emotions ranked top five. 2.1 Monitoring Public Opinion Related to EMR: Enhancing the Monitoring System and Considering Cluster Density  
We comprehensively considered cluster density. To provide precise and effective early warnings of public opinion dissemination, intervention is unnecessary for clusters with low density or those that have not resulted in small-scale malicious dissemination. Emotional dissemination dynamics within clusters are driven by emotional resonance triggered by emotional contagion. The density and trustworthiness of clusters forming the dissemination chain during the first and second dissemination stages determine whether negative emotions can resonate. To gain deeper insights into the dissemination characteristics of negative public opinion related to electromagnetic radiation, this study analyzes cluster density. Table 3 lists the top five Weibo posters with the highest negative comprehensive emotion values and the top five posters with the highest average macro cluster emotion values. The average macro cluster emotion value reflects only the emotional tendency of comment texts, without considering cluster density or trustworthiness. The results indicate that the top five Weibo posts in terms of average macro cluster emotion values did not appear in the comprehensive emotion rankings. Although these posts had highly negative first-level comments, the low macro cluster density prevented even small-scale malicious dissemination. Furthermore, these posts had little second-level feedback, indicating low micro cluster density. As a result, the negative comprehensive emotion values were not high, and there was no malicious dissemination. In practical public opinion monitoring, there is no need for early warnings in such cases.