Research on the quantitative model of the communication effect of Civic and Political Education for social platform data

Social platforms have revolutionized the lives of the public, provided a resourceful educational platform for the ideological and political education of college students, and become the distribution center of ideological and political education information and the amplifier of social opinion. In this paper, we concentrate on the dissemination of data related to ideological and political education on social platforms. We start by segmenting the text and then use the IF-IDF algorithm, the Text Rank algorithm, and the LDA theme model to process various text features. Simultaneously, we will employ a sentiment dictionary and simple Bayesian analysis, along with a convolutional neural network, to measure the impact of emotion propagation. The microblogging user’s comment data for the Civics communication emotion analysis concluded that the user group of the initial period of the Civics education activities will produce some negative emotions, while at the same time, the positive emotion has been maintaining an upward trend. While the negative emotions began to decline and slow down, the user group with the highest positive emotion reached a value of 6.35464.