%A Wang Li,Zou Lixue,Liu Xiwen %T Visualizing Document Correlation Based on LDA Model %0 Journal Article %D 2018 %J Data Analysis and Knowledge Discovery %R 10.11925/infotech.2096-3467.2017.1058 %P 98-106 %V 2 %N 3 %U {https://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/abstract/article_4490.shtml} %8 2018-03-25 %X

[Objective] This paper tries to construct data analysis model for the topics of scientific research based on machine learning. [Methods] First, we clustered data with the Latent Dirichlet Allocation model. Then, we investigated the correlation among year, institution and research types with the help of Python modules. Finally, we revealed and visualized the key research areas of every year or institution. [Results] We analyzed 101,813 papers and patents of graphene industray research. The proposed method finished the topic identification, correlation analysis, and visualization in about two miniutes. [Limitations] More research is needed to explore the network analysis issues. [Conclusions] Machine learning provides enormous potentiality for intelligence studies, especially the large volume text analytics and visualization.