Empirical research on the evolution trend of heat and sentiment for emergencies
Emergencies inflict heavy casualties, economic losses, ecological damage, and significant social harm to society. By segmenting information topics and analysing emotional shifts, we can identify corresponding real-world events and their impacts, thereby providing guidance for timely responses to eme...
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| Format: | Article |
| Language: | English |
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KeAi Communications Co., Ltd.
2025-12-01
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| Series: | International Journal of Cognitive Computing in Engineering |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666307425000063 |
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| author | Shihong Wu Wei Yu Yanxia Zhao Hongyan Li Jiatong Wang Weiyan Yang Yue Lin |
| author_facet | Shihong Wu Wei Yu Yanxia Zhao Hongyan Li Jiatong Wang Weiyan Yang Yue Lin |
| author_sort | Shihong Wu |
| collection | DOAJ |
| description | Emergencies inflict heavy casualties, economic losses, ecological damage, and significant social harm to society. By segmenting information topics and analysing emotional shifts, we can identify corresponding real-world events and their impacts, thereby providing guidance for timely responses to emergencies. In the past, public opinion monitoring of emergencies was based mainly on single-topic detection or emotion analysis, which cannot comprehensively evaluate the evolution of public opinion. In this work, word segmentation is applied to video comments related to various emergency situations. By utilizing the co-word network and Louvain algorithm for theme division, along with sentiment analysis constructed through time series analysis of sentiment value changes for various emergencies employing the naive Bayes method, the evolution of public opinion is comprehensively assessed. As a result, the pivotal nodes in the evolution of public opinion are identified and the evolution process is divided into stages. Using this method, relevant management departments can effectively address the majority of public opinions for various types of emergencies, addressing them from the perspectives of prevention, adjustment, and recovery. This approach not only enhances rescue efficiency and strengthens safety management but also actively guides the evolution of public opinion, ultimately providing society with solid and reliable security safeguards. |
| format | Article |
| id | doaj-art-00c60fe1af904fce8394f9081a41fbdd |
| institution | Kabale University |
| issn | 2666-3074 |
| language | English |
| publishDate | 2025-12-01 |
| publisher | KeAi Communications Co., Ltd. |
| record_format | Article |
| series | International Journal of Cognitive Computing in Engineering |
| spelling | doaj-art-00c60fe1af904fce8394f9081a41fbdd2025-02-06T05:12:50ZengKeAi Communications Co., Ltd.International Journal of Cognitive Computing in Engineering2666-30742025-12-016323332Empirical research on the evolution trend of heat and sentiment for emergenciesShihong Wu0Wei Yu1Yanxia Zhao2Hongyan Li3Jiatong Wang4Weiyan Yang5Yue Lin6School of International Business, Zhejiang Yuexiu University, Shaoxing, 312069, China; Shaoxing Key Laboratory of Intelligent Monitoring and Prevention of Smart City, Shaoxing, 312069, ChinaSchool of International Business, Zhejiang Yuexiu University, Shaoxing, 312069, China; Shaoxing Key Laboratory of Intelligent Monitoring and Prevention of Smart City, Shaoxing, 312069, ChinaFinance Office, Zhejiang Yuexiu University, Shaoxing 312069, China; School of Public Administration, Zhejiang Gongshang University, Hangzhou, 310018, China; Correspondence to: Qunxian Middle Road NO.2801, Shaoxing, Zhejiang, ChinaSchool of International Business, Zhejiang Yuexiu University, Shaoxing, 312069, China; Shaoxing Key Laboratory of Intelligent Monitoring and Prevention of Smart City, Shaoxing, 312069, ChinaSchool of International Business, Zhejiang Yuexiu University, Shaoxing, 312069, ChinaSchool of International Business, Zhejiang Yuexiu University, Shaoxing, 312069, China; Shaoxing Key Laboratory of Intelligent Monitoring and Prevention of Smart City, Shaoxing, 312069, ChinaSchool of International Business, Zhejiang Yuexiu University, Shaoxing, 312069, China; Shaoxing Key Laboratory of Intelligent Monitoring and Prevention of Smart City, Shaoxing, 312069, ChinaEmergencies inflict heavy casualties, economic losses, ecological damage, and significant social harm to society. By segmenting information topics and analysing emotional shifts, we can identify corresponding real-world events and their impacts, thereby providing guidance for timely responses to emergencies. In the past, public opinion monitoring of emergencies was based mainly on single-topic detection or emotion analysis, which cannot comprehensively evaluate the evolution of public opinion. In this work, word segmentation is applied to video comments related to various emergency situations. By utilizing the co-word network and Louvain algorithm for theme division, along with sentiment analysis constructed through time series analysis of sentiment value changes for various emergencies employing the naive Bayes method, the evolution of public opinion is comprehensively assessed. As a result, the pivotal nodes in the evolution of public opinion are identified and the evolution process is divided into stages. Using this method, relevant management departments can effectively address the majority of public opinions for various types of emergencies, addressing them from the perspectives of prevention, adjustment, and recovery. This approach not only enhances rescue efficiency and strengthens safety management but also actively guides the evolution of public opinion, ultimately providing society with solid and reliable security safeguards.http://www.sciencedirect.com/science/article/pii/S2666307425000063Internet public opinionAnomaly detectionTopic heatTime seriesSentiment analysis |
| spellingShingle | Shihong Wu Wei Yu Yanxia Zhao Hongyan Li Jiatong Wang Weiyan Yang Yue Lin Empirical research on the evolution trend of heat and sentiment for emergencies International Journal of Cognitive Computing in Engineering Internet public opinion Anomaly detection Topic heat Time series Sentiment analysis |
| title | Empirical research on the evolution trend of heat and sentiment for emergencies |
| title_full | Empirical research on the evolution trend of heat and sentiment for emergencies |
| title_fullStr | Empirical research on the evolution trend of heat and sentiment for emergencies |
| title_full_unstemmed | Empirical research on the evolution trend of heat and sentiment for emergencies |
| title_short | Empirical research on the evolution trend of heat and sentiment for emergencies |
| title_sort | empirical research on the evolution trend of heat and sentiment for emergencies |
| topic | Internet public opinion Anomaly detection Topic heat Time series Sentiment analysis |
| url | http://www.sciencedirect.com/science/article/pii/S2666307425000063 |
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