Quantification and Analysis of Group Sentiment in Electromagnetic Radiation Public Opinion Events
This research focuses on developing a sentiment-based system to analyze public opinion on electromagnetic radiation in online networks. Issues related to EMR, such as the NIMBY effect and negative public sentiment, can lead to health crises, social conflicts, and challenges in decision-making. This...
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MDPI AG
2025-05-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/15/9/5209 |
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| author | Qinglan Wei Xinyi Ling Jiqiu Hu |
| author_facet | Qinglan Wei Xinyi Ling Jiqiu Hu |
| author_sort | Qinglan Wei |
| collection | DOAJ |
| description | This research focuses on developing a sentiment-based system to analyze public opinion on electromagnetic radiation in online networks. Issues related to EMR, such as the NIMBY effect and negative public sentiment, can lead to health crises, social conflicts, and challenges in decision-making. This study addresses limitations in existing research, including inaccurate data collection and a lack of systematic analysis. By incorporating Jieba Chinese word segmentation technology, this study introduces an innovative data collection method based on topic similarity, significantly improving data accuracy. Additionally, this research establishes a comprehensive public opinion analysis framework that integrates user follower counts, geographical distribution, and interaction data. This framework facilitates the identification of sources of negative sentiment and the development of effective response strategies. As a case study, the dissemination patterns of EMR-related public opinion on Weibo are analyzed, focusing on group sentiment and social interaction. The proposed system achieves a 65.85% improvement in data collection accuracy, demonstrating its effectiveness. Furthermore, this study provides actionable recommendations for relevant departments and governments to monitor, analyze, and respond to EMR-related public opinion. By enhancing decision-making and protecting public interests, this study highlights the role of technology in improving social governance and substantial development. |
| format | Article |
| id | doaj-art-287f0e864d2e4d3882ffc4307c766fcc |
| institution | Kabale University |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-287f0e864d2e4d3882ffc4307c766fcc2025-08-20T03:52:56ZengMDPI AGApplied Sciences2076-34172025-05-01159520910.3390/app15095209Quantification and Analysis of Group Sentiment in Electromagnetic Radiation Public Opinion EventsQinglan Wei0Xinyi Ling1Jiqiu Hu2The School of Data Science and Intelligent Media, Communication University of China, Beijing 100024, ChinaThe School of Data Science and Intelligent Media, Communication University of China, Beijing 100024, ChinaThe Department of Computing, Imperial College of London, Exhibition Rd, South Kensington, London SW7 2AZ, UKThis research focuses on developing a sentiment-based system to analyze public opinion on electromagnetic radiation in online networks. Issues related to EMR, such as the NIMBY effect and negative public sentiment, can lead to health crises, social conflicts, and challenges in decision-making. This study addresses limitations in existing research, including inaccurate data collection and a lack of systematic analysis. By incorporating Jieba Chinese word segmentation technology, this study introduces an innovative data collection method based on topic similarity, significantly improving data accuracy. Additionally, this research establishes a comprehensive public opinion analysis framework that integrates user follower counts, geographical distribution, and interaction data. This framework facilitates the identification of sources of negative sentiment and the development of effective response strategies. As a case study, the dissemination patterns of EMR-related public opinion on Weibo are analyzed, focusing on group sentiment and social interaction. The proposed system achieves a 65.85% improvement in data collection accuracy, demonstrating its effectiveness. Furthermore, this study provides actionable recommendations for relevant departments and governments to monitor, analyze, and respond to EMR-related public opinion. By enhancing decision-making and protecting public interests, this study highlights the role of technology in improving social governance and substantial development.https://www.mdpi.com/2076-3417/15/9/5209network public opinionelectromagnetic radiationNIMBY effectgroup emotionsquantification model |
| spellingShingle | Qinglan Wei Xinyi Ling Jiqiu Hu Quantification and Analysis of Group Sentiment in Electromagnetic Radiation Public Opinion Events Applied Sciences network public opinion electromagnetic radiation NIMBY effect group emotions quantification model |
| title | Quantification and Analysis of Group Sentiment in Electromagnetic Radiation Public Opinion Events |
| title_full | Quantification and Analysis of Group Sentiment in Electromagnetic Radiation Public Opinion Events |
| title_fullStr | Quantification and Analysis of Group Sentiment in Electromagnetic Radiation Public Opinion Events |
| title_full_unstemmed | Quantification and Analysis of Group Sentiment in Electromagnetic Radiation Public Opinion Events |
| title_short | Quantification and Analysis of Group Sentiment in Electromagnetic Radiation Public Opinion Events |
| title_sort | quantification and analysis of group sentiment in electromagnetic radiation public opinion events |
| topic | network public opinion electromagnetic radiation NIMBY effect group emotions quantification model |
| url | https://www.mdpi.com/2076-3417/15/9/5209 |
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