ABSTRACT
The paper uses the Latent Dirichlet Allocation (LDA) topic model to study the change law of hot topics in the field of digital economy and show the research topic evolution in this field over the last 30 years. The data consists of 1823 papers from various journals from China National Knowledge Infrastructure (CNKI). The main results of LDA are as follows. First, we find five research topics to cover this research field. Second, among these topics, we find that two topics show an upward trend in intensity and another two show an downward trend in intensity, and high-quality development and governance of digital economy are new topics in recent years. It is hoped that the findings could help scholars to overview the field of digital economy and grasp the emerging evolution trends.
- Jiang Xiaojuan. Jiang Xiaojuan: the development trend and governance focus of digital economy during the 14th five-year Plan period [J]. Shandong Economic Strategy Research, 2020, (10): 50-52.Google Scholar
- Luo Jian, Zhu Xinmin. The development trend of foreign digital economy and the national development strategy of digital economy [J]. Scientific and technological Progress and Countermeasures, 2013,(08): 124-128.Google Scholar
- Wang Weiling. Research on the Development trend and Promotion Policy of Digital economy in China [J]. Economic Review Journal,2019,(01): 69-75.Google Scholar
- Chen Xiaohua. Development trend of digital economy and block chain in 5G era [J]. Leading Science Forum, 2020,(16): 18-45.Google Scholar
- Peng Xinyong, Cai Yaojun, Zhao Qin. Research and judgment on the eight Trends of Digital economy Development in Guangxi [J]. Market Forum, 2020,(10): 7-10+18Google Scholar
- Blei D M , Ng A Y , Jordan M I , Latent Dirichlet Allocation[J]. Journal of Machine Learning Research, 2003,(3):993-1022.Google Scholar
- Debin Fang, Haixia Yang, Baojun Gao, Xiaojun Li.Discovering research topics from library electronic references using latent Dirichlet allocation. Library Hi Tech,2018,(1):1-12.Google Scholar
- Misra H, Yvon F, Cappé O, et al. Text segmentation: A topic modeling perspective[J]. Information Processing & Management,2011,47( 4) : 528-544.Google Scholar
- Ding Y . Topic‐based PageRank on author cocitation networks[J]. Journal of the American Society for Information Science & Technology, 2014, 62(3):449-466.Google Scholar
- Sugimoto C R , Li D , Russell T G , The shifting sands of disciplinary development: Analyzing North American Library and Information Science dissertations using latent Dirichlet allocation[J]. Journal of the Association for Information Science & Technology, 2014, 62(1):185-204.Google Scholar
- Wu Xiaofeng, Zong Chengqing. A CRF automatic abstracting method based on LDA [J]. Journal of Chinese Information, 2009,23 (6): 39-46.Google Scholar
- Shi Jing, Li Wanlong. Subject word extraction method based on LDA model [J]. Computer Engineering, 2010, (19): 87-89.Google Scholar
Index Terms
- Research on the Topic Evolution of Digital Economy Based on LDA
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