# Quantification and Analysis of Group Sentiment in Electromagnetic Radiation Public Opinion Events
## 1 Introduction  
In recent years, the internet industry in China has seen rapid development, with the number of internet users steadily increasing and the new media environment becoming more diverse. According to the 53 rd \"Statistical Report on the Development of the Internet in China\", ref. [1] as of December 2023, there were 1.092 billion Internet users in China, with an average of 26.1 h of internet use per week per user. Platforms such as Weibo, WeChat, and Douyin, which previously dominated the social media landscape, have seen new competition from platforms like Kuaishou, Zhihu, Bilibili, and Xiaohongshu, all of which have gained significant traction due to their large user bases, ease of use, real-time information sharing, and strong interactive capabilities. One particularly sensitive topic in online discussions is electromagnetic radiation (EMR), which is often linked with public concern over potential health hazards and the proliferation of EMR-emitting infrastructure such as mobile phone towers and substations. Although scientific evidence does not conclusively confirm that long-term harm can come from everyday EMR exposure, public apprehension remains widespread. This sentiment is frequently amplified by misinformation and media sensationalism, giving rise to the \"Not in My Backyard\" (NIMBY) effect. In the context of EMR, this manifests as collective opposition to infrastructure projects near residential areas, which can hinder technological development and escalate into public opinion crises online. When left unaddressed, these emotional responses may intensify social tensions, delay construction, and lead to costly policy reversals. Effective public opinion management in such scenarios requires timely identification of emerging concerns, accurate measurement of group sentiment, and appropriate response strategies. However, current approaches to analyzing online sentiment often rely on simple keyword matching, which lacks contextual understanding and leads to irrelevant or inaccurate results. Moreover, there is a lack of targeted frameworks for quantifying group emotions specific to EMR-related public discourse. Previous studies have addressed public opinion or sentiment analysis in some respects. Social psychologist [PERSON] [2] proposed the Social Influence Theory (SIT), which examines how groups influence individual behavior, suggesting that a person's thoughts, emotions, and attitudes are affected by others. [PERSON] et al. [3] explained the nature of emotions based on group identity in their group emotion theory. [PERSON] et al. [4] suggested that as group size increases, individuals capable of emotional contagion become less sensitive to their environment. These theories are also relevant to the study of network public opinion. In network public opinion events, emotions play a crucial role in driving the evolution of the events. The academic community has recognized the significance of netizens' emotions in public opinion crises and has conducted extensive research. Researchers have constructed a network public opinion propagation model that considers individual emotion, and the main factors affecting the formation and propagation of network public opinion are discussed through simulation experiments [5]. [PERSON] et al. [6] used social networks to track the evolution of public emotion during the COVID-19 pandemic in China and analyze the root causes of these public emotions from an event-driven perspective. To address the issues, this study will describe an opinion analysis and guidance system for the electromagnetic NIMBY effect, leveraging sentiment computing. As illustrated in Figure 1, our work is explained in Section 3 and can be summarized as follows: we have designed an accurate data collection module based on topic similarity and developed a public opinion analysis system for EMR events. Figure 1: Research on electromagnetic NIMBY effect public opinion analysis system based on affective computing.