Incorporating LDA Based Text Mining to Explore New Energy Vehicles in China

被引：14

作者：

Jia, Susan ^{[1
]}

Wu, Banggang ^{[2
]}

机构：

[1] Shanghai Int Studies Univ, Sch Business & Management, Shanghai 200083, Peoples R China

[2] Wuhan Univ, Econ & Management Sch, Wuhan 430072, Hubei, Peoples R China

来源：

IEEE ACCESS | 2018年 / 6卷

基金：

中国国家自然科学基金;

关键词：

Battery; China; full electric vehicle; LDA; new energy vehicle; text mining; POLICIES;

D O I：

10.1109/ACCESS.2018.2877716

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Tracking the evolution of policy and development of new energy vehicles (NEVs) in China is of critical significance, because it helps generate rational prediction regarding future trends. To this end, this paper investigated the 5185 articles on NEV obtained from China National Knowledge Infrastructure by means of latent Dirichlet allocation (LDA)-based text mining. Word count was performed to highlight important keywords for different periods of years of publication. In addition, topics were identified from the abstracts of these articles using LDA. Findings suggest that attention on NEV in China has been growing and will continue to grow in the predictable future. Full electric vehicle, being the currently dominating form of NEV, will continue to play the leading role. Meanwhile, China's NEV industry requires further investment into charging, battery, personnel training, and patent portfolio.

引用

页码：64596 / 64602

页数：7

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