Information security topics extraction and classification method based on modified LDA model

Abstract

With the rapid development of social informatization, information plays a pivotal role in people’s lives. The popularization of information has also brought serious information security problems, information security incidents become hot topics for the public. However, it is difficult for the researchers to quickly locate information security incidents because the information security topics are overwhelmed by massive news topics. To solve this problem, a modified LDA model using Python is proposed to improve the classification effect and accuracy of information security topics by perfecting the determination index of the number of topics. Simulation results show that the proposed method can obtain better classification performance compared with the traditional text classification method.