LDA-Bert based public opinion subject mining analysis of emergencies

Abstract

Studying the theme of emergencies is of great significance to the emergency management of subsequent public opinion. In order to solve the problem that LDA ignores the context semantics, and the topic distribution is biased towards high-frequency words, Bert is introduced. LDA-Bert public opinion topic mining model is proposed. First, LDA is used to select candidate words; Then, Bert is used to construct candidate word vectors and topic vectors with context semantics, and the topic keywords are filtered twice by cosine similarity calculation; Finally, the corresponding public opinion response strategies are proposed through the subject mining results of different life cycles. Taking the "Xi'an epidemic" as an example, the experiment proved that the model can effectively extract theme keywords, providing a strong basis for the follow-up analysis of theme changes at different stages of public opinion.