Abstract：As a hot topic, food safety has attracted a lot of attention from the public, and microblog has become the main media platform to expose food safety incidents. Microblog corpus was used as data source with microblog content and user social network behavior characteristics, and the food safety incident discovery method was proposed based on the momentum model. To describe the food safety incident from microblog information flow, the event discovery model was used to detect the candidate feature words related to food safety. The momentum model was established to realize the momentum modeling of candidate feature words and filter the duplicate feature words effectively. The effective feature words were classified and merged by Kmeans clustering, and the goal of discovering food safety incidents was achieved. The experimental results show that the proposed method can effectively discover the food safety incidents spreading in microblog and filter out irrelevant topics in microblog.
2019, Vol. 40 
