The research topics identification with multiple data source based on causal regression

Abstract:

In order to effectively tackle the research topics identification with multiple data source, a new research topic identification method is presented based on causal regression. In this paper, the evaluation indicators are defined to identify the key parameters of research topics for multiple data source, such as the citation weight and status density of research topics, the feature function is established with morphological characteristics of research topics, and the research topics identification based on multiple data sources is modeled by causal regression. The experimental results show that the proposed method has great advantages in terms of value citation, citation weight and similarity with frontier topics, compared with Naive Bayes, KNN and Mge LDA algorithm.