Analysis of Emotional Evolution of Emergent Events Network Based on Spatial Measurement

Abstract

I study the temporal and spatial evolution of network emotions in emergencies, and provide decision support for public opinion guidance. This paper uses python to compile a crawler to obtain the comment information of Weibo, calculates its network sentiment value based on the naive Bayes classifier, uses spatial analysis methods and the SAAR spatial measurement model system, and analyzes the temporal and spatial pattern evolution and spatial agglomeration of network sentiment Characteristics, and further reveals its influencing factors and complex spatial correlation. At the same time, there is a spatial spillover effect of online emotions, and the intensity of comments and public opinion has a promoting effect on it, while geographical distance has an obvious inhibitory effect on it. Online emotions have regional differences. Disaster management departments can use this difference to more efficiently identify emotionally hot areas from social media data, so as to take timely response measures.