Research on sentiment analysis of public opinion events based on human behavior dynamics

Abstract

With the rapid development of the Internet and social networks, how to analyze the behavior trajectory, emotional changes and relationship trends of online public opinion events have become an important research topic today. This article combines human behavior dynamics and sentiment analysis methods to study online public opinion events. This article uses Python to capture hot public opinion topics and comments on social platforms, and uses human dynamics to analyze public opinion events from time interval distribution and activity. Then this paper uses the maximum likelihood estimation method to evaluate the power exponent of its distribution, and finally uses BosonNLP and sentiment intensity to analyze the sentiment of the comment objects in the public opinion event. Experimental results show that the time interval of group public opinion events obeys a power-law distribution, and its activity is positively correlated with the power exponent. The sentiment analysis method of public opinion events based on human behavior dynamics performs well, and the dominant sentiment of the comment object is distributed with a power exponential. The number of likes and the sentiment value of follow-up comments effectively improve the results of sentiment analysis, and social platforms play an important role in the communication of sentiments.