Emerging research topics detection with multiple machine learning models |
| |
| Authors: | Shuo Xu Liyuan Hao Xin An Guancan Yang Feifei Wang |
| |
| Institution: | 1. Research Base of Beijing Modern Manufacturing Development, College of Economics and Management, Beijing University of Technology, No. 100 PingLeYuan, Chaoyang District, Beijing 100124, PR China;2. School of Economics and Management, Beijing Forestry University, No. 35 Qinghua East Road, Haidian District, Beijing 100083, PR China;3. School of Information Resource Management, Renmin University of China, No. 59 Zhongguancun Street, Haidian District, Beijing 100872, PR China |
| |
| Abstract: | Emerging research topic detection can benefit the research foundations and policy-makers. With the long-term and recent interest in detecting emerging research topics, various approaches are proposed in the literature. Though, there is still a lack of well-established linkages between the clear conceptual definition of emerging research topics and the proposed indicators for operationalization. This work follows the definition by Wang (2018), and several machine learning models are together used to detect and foresight the emerging research topics. Finally, experimental results on gene editing dataset discover three emerging research topics, which make clear that it is feasible to identify emerging research topics with our framework. |
| |
| Keywords: | Corresponding author Emerging research topics Topic modeling Dynamic Influence Model Citation Influence Model Machine learning |
| 本文献已被 ScienceDirect 等数据库收录！ |
|
