Sentiment Analysis of Weibo Comments based on LDA Model

Abstract

The essence of LDA (Latent Dirichlet Allocation) model is a generative Bayesian probability model that contains three layers of words, topics and corpus (sometimes called document set). Under the LDA algorithm theory, each document represents a probability distribution formed by some topics, and each topic represents a probability distribution formed by many words. Therefore, the model fitting results will present the core keywords and specific probabilities of each topic, and researchers can interpret the meaning of the document according to the model results. In this paper, we hope to use the LDA model as the basis for the emotional analysis of microblog comments.

Difference in Rumor Dissemination and Debunking Before and After the Relaxation of COVID-19 Prevention and Control Measures in China: Infodemiology Study

“…Simulation models for analyzing rumor content have also advanced. For instance, Xi and Yong [18] proposed a model for public opinion dissemination on social networks, Han et al [19] analyzed network topology in rumor spreading, Wu et al [20] developed a rumor-spreading model based on recommendation systems, and Fang and Yang [21] studied rumor spreading and refutation effectiveness on Weibo under real-name systems. Moreover, epidemic models have been applied to social network rumor propagation studies, based on similarities between rumor spread and disease transmission mechanisms [22], including SI (susceptible and infectious) [23][24][25], SIS (susceptible, infectious, and susceptible) [26][27][28], SIR (susceptible, infectious, and recovered) [29][30][31], and SEIR (susceptible, exposed, infectious, recovered) [32][33][34] models, representing susceptible, infected, recovered, and exposed states, respectively [35].…”