School of Political Science and Law, Weifang University, Weifang, Shandong 261061, China.
J Environ Public Health. 2022 Aug 23;2022:6367295. doi: 10.1155/2022/6367295. eCollection 2022.
At present, China is in the period of social transformation, and social contradictions are gradually prominent. The research on NPO (network public opinion) emergency warning methods is gradually increasing. Some existing laws and regulations are abstracted and principled in content, lacking specific implementation rules and corresponding supporting measures, especially the legal rules of emergency administrative procedures. Therefore, the legal early warning model of NPO public crisis is based on emotional dimension content, NPO emotional characteristics, emotional dimension elements, and machine learning classification algorithm to construct text ET (emotional tendencies) classifier, which can be used to make ET judgment on text data. The results show that after PSO (particle swarm optimization) algorithm optimization, the precision, recall rate, and micro-average are significantly improved, and the precision is increased by nearly 14% and 80%. The conclusion shows that using PSO optimization parameters improves the classification effect of the classifier, and a better NPO crisis early warning model can be obtained.
目前，中国正处于社会转型期，社会矛盾逐渐凸显。关于 NPO（网络舆情）应急预警方法的研究逐渐增多。一些现有的法律法规在内容上被抽象和原则化，缺乏具体的实施细则和相应的配套措施，特别是应急行政程序的法律规则。因此，基于情感维度内容、NPO 情感特征、情感维度要素和机器学习分类算法构建文本 ET（情感倾向）分类器，对文本数据进行 ET 判断的 NPO 公共危机法律预警模型，可以用于对文本数据进行 ET 判断。结果表明，经过 PSO（粒子群优化）算法优化后，准确率、召回率和微平均值均有显著提高，准确率提高了近 14%和 80%。结论表明，使用 PSO 优化参数可以提高分类器的分类效果，从而得到更好的 NPO 危机预警模型。
