# Quantification and Analysis of Group Sentiment in Electromagnetic Radiation Public Opinion Events
## 1 Introduction
During the data collection process, Weibo v12.12.3 was selected as the primary data source due to its status as the largest social media platform in China, where hundreds of millions of users express their opinions daily. Using Python 3, we crawled posts related to electromagnetic radiation (EMR) published on Weibo in recent years, including associated metadata such as comments, reposts, and likes. In the next step, the collected data were stored in a structured database. To enhance the accuracy of data collection, we applied Baidu's familia semantic similarity technology for relevance filtering, resulting in a large, high-quality dataset of authentic and valid EMR-related content. In the public opinion analysis stage, we conducted both fine-grained public opinion analysis and domain-customized public opinion analysis. In the fine-grained analysis, the system generated insights such as group sentiment ranking, a geographic sentiment distribution map of China, cluster density ranking, a like-comment-forward ratio chart, and group sentiment trend graphs over time. In the domain-customized analysis, the system focused on hot topic mining and traceability analysis, enabling deeper understanding of discussion focal points and the origin paths of public opinion related to EMR. This paper applies and integrates the following methods within the context of EMR-related public sentiment analysis:  
* A public opinion analysis system based on sentiment computing for electromagnetic NIMBY effects is proposed. The system consists of three core functional modules: data collection, emotional intelligence analysis, and in-depth public opinion analysis. It uses a dictionary-based sentiment calculation algorithm to present sentiment and social behavior analysis results through various charts, including bar charts, trend charts, and pie charts. By extensively collecting user data and posts, the system provides a comprehensive view of public sentiment. Due to the high sensitivity and risks of EMR-related public opinion, the system includes specialized functions for hot topic mining and source tracing. These features help accurately identify negative sentiment origins and enhance public opinion monitoring and management efficiency. * A data collection system based on topic similarity is designed: This system overcomes the limitation of traditional web crawlers that rely only on keyword searches, which do not ensure content relevance. It uses semantic matching technology instead of simple keyword matching, improving information filtering accuracy by 65.85%. This enhancement significantly boosts the precision of data collection, providing more reliable support for subsequent public opinion analysis. * Using this system, case studies on public opinion related to EMR events have been conducted: The unique aspects have been thoroughly investigated, leading to the development of a comprehensive and effective strategy for managing network public opinion. This strategy offers valuable insights for relevant departments. Public opinion monitoring refines the monitoring system by incorporating factors such as cluster density. Public opinion analysis focuses on both the origins and dissemination processes of public opinion, which help to establish a better understanding of public needs, allow for more effective mining of opinion data, and enhance timeliness and specificity in guiding public sentiment. Public opinion response advocates for mainstream and local media to work together in addressing public opinion challenges effectively. The remainder of this paper is structured as follows: Section 2 reviews previous research on online sentiment analysis and the NIMBY effect in the context of electromagnetic radiation. Section 3 describes the design and implementation of the proposed sentiment-based public opinion analysis system, including data collection and emotional modeling. Section 4 presents the research design, experimental process, and analysis of sentiment dissemination.