{"title":"Emergency Network Public Opinion and Coping Strategies Based on Emotion Feature Extraction Algorithm","authors":"","doi":"10.25236/ajcis.2023.061009","DOIUrl":null,"url":null,"abstract":"With the development of the Internet in today's society, the prevalence of public opinion also heralds the trend of networking. More and more people are starting to express their opinions and opinions on the Internet. Therefore, the analysis of emergency Internet public opinion and the research on coping strategies are becoming more and more important. Although there are many studies on emergency Internet public opinion analysis and coping strategies, the existing research still needs to be supplemented. This article was a certain discussion on the empathy strategy of characteristic diplomatic language. First, the relevant background of the title was introduced at the beginning of the introduction section. Emergency Internet public opinion analysis and coping strategies were analyzed and studied, and thinking was made. Second, various algorithms were proposed. The algorithm was established based on the emotion feature extraction algorithm, which has provided a theoretical basis. Third, for the response methods of emergency Internet public opinion, the emergency characteristics of college Internet public opinion in the era of big data were introduced. The application of big data in Internet public opinion has carried out emergency Internet public opinion analysis research. Finally, this paper conducted experimental research on the subject of emergency Internet public opinion analysis and coping strategies research based on emotion feature extraction algorithm. The research results showed that the research model constructed in this paper has improved the effectiveness of emergency Internet public opinion by 15.12%.","PeriodicalId":387664,"journal":{"name":"Academic Journal of Computing & Information Science","volume":"140 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Academic Journal of Computing & Information Science","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.25236/ajcis.2023.061009","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
With the development of the Internet in today's society, the prevalence of public opinion also heralds the trend of networking. More and more people are starting to express their opinions and opinions on the Internet. Therefore, the analysis of emergency Internet public opinion and the research on coping strategies are becoming more and more important. Although there are many studies on emergency Internet public opinion analysis and coping strategies, the existing research still needs to be supplemented. This article was a certain discussion on the empathy strategy of characteristic diplomatic language. First, the relevant background of the title was introduced at the beginning of the introduction section. Emergency Internet public opinion analysis and coping strategies were analyzed and studied, and thinking was made. Second, various algorithms were proposed. The algorithm was established based on the emotion feature extraction algorithm, which has provided a theoretical basis. Third, for the response methods of emergency Internet public opinion, the emergency characteristics of college Internet public opinion in the era of big data were introduced. The application of big data in Internet public opinion has carried out emergency Internet public opinion analysis research. Finally, this paper conducted experimental research on the subject of emergency Internet public opinion analysis and coping strategies research based on emotion feature extraction algorithm. The research results showed that the research model constructed in this paper has improved the effectiveness of emergency Internet public opinion by 15.12%.