| Sumario: | 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.
|
|---|