Abstract

The authors of this article use text mining techniques to uncover hidden or latent topics in economic education. The common use of JEL codes only identifies the academic setting for each paper but does not identify the underlying economic concept the paper addresses. An unsupervised machine learning algorithm called Latent Dirichlet Allocation is utilized to identify 15 hidden topics in economic education scholarly work. The text mining model identifies economic education topics by finding correlations in word usage across different documents. The authors show that these newly identified research topics explain more variation in citation counts than the commonly adopted JEL codes. Moreover, specific journals display preferences for certain topics within economic education research.

摘要

本文的作者使用文本挖掘技术来揭示经济教育中的隐藏或潜在主题。JEL代码的常见用法仅标识每篇论文的学术背景，而不能标识论文所针对的基本经济概念。一种称为Latent Dirichlet Allocation的无监督机器学习算法可用于识别经济教育学术工作中的15个隐藏主题。文本挖掘模型通过查找不同文档中单词使用的相关性来识别经济教育主题。作者表明，这些新近确定的研究主题比通常采用的JEL代码解释了更多的引文计数变化。此外，特定期刊显示了经济教育研究中某些主题的偏好。