采用LDA主题模型的国内知识流研究结构探讨: 以学科分类主题抽取为视角

Abstract: [Objective] This paper aims to comprehensively explore the knowledge structure and hotspot distribution of different disciplines, with the help of topic extraction and distribution analysis using LDA (Latent Dirichlet Allocation) model from the perspective of subject classification. [Methods] We collected data from the domestic knowledge flow (KF) field and KF related literature from CNKI and Wangfang database, then grouped these data into 11 categories by Chinese Library Classification. Finally, we extracted 20 hot subjects from documents in 11 disciplines with the help of the LDA topic model. [Results] The content and knowledge points in 11 disciplines were obtained from the analysis of 20 extracted hot topics. [Limitations] We did not compare the proposed method with topic mining research in other fields. The domestic KF hotspots found by our study were not compared to the previous findings. [Conclusions] The proposed method can help us explore the knowledge structure and research trends more comprehensively.