Social Network Emergency Incident Portrait Based on Attention Mechanism

Abstract

With the development of social networks, more and more people use social networks to publish and disseminate national security emergencies. In order to effectively control the spread and development of Chinese social network national security emergencies, we need to make an effective portrait of the emergencies. However, Chinese social network information has two research difficulties, such as text irregularity and few data sets in related fields, which may result in inaccurate event portrait results. In order to solve the above problems, we propose an algorithm based on the attention mechanism of Chinese part-of-speech tagging results (BLTAC) to perform emergency event portrait of Chinese social networks, which can efficiently perform emergency portraits. The BLTAC algorithm can be used to extract the Chinese social network emergency text entity name, and use the extracted entity name to describe the emergency event to perform event portrait. The experimental results show that the F1-score of our algorithm for the entity names recognition in each category on the Weibo dataset is improved compared with the other methods.