Emerging research topics detection with multiple machine learning models

作者：

Highlights：

• Several machine learning models are together used to detect and foresight the emerging research topics.

• The following indicators are operationalized: radical novelty, relatively fast growth, coherence and scientific impact.

• As for the CIM model, the collapsed Gibbs sampling is done separately for the cited and citing publication parts.

• Experimental results on gene editing dataset show that it is feasible to identify emerging research topics with our framework.

摘要

•Several machine learning models are together used to detect and foresight the emerging research topics.•The following indicators are operationalized: radical novelty, relatively fast growth, coherence and scientific impact.•As for the CIM model, the collapsed Gibbs sampling is done separately for the cited and citing publication parts.•Experimental results on gene editing dataset show that it is feasible to identify emerging research topics with our framework.

论文关键词：Emerging research topics,Topic modeling,Dynamic Influence Model,Citation Influence Model,Machine learning

论文评审过程：Received 13 February 2019, Revised 15 October 2019, Accepted 16 October 2019, Available online 26 November 2019, Version of Record 26 November 2019.

论文官网地址：https://doi.org/10.1016/j.joi.2019.100983