This research aims to illustrate the potential use of concepts, techniques, and mining process tools to improve the systematic review process. Thus, a review was performed on two online databases (Scopus and ISI Web of Science) from 2012 to 2019. A total of 9649 studies were identified, which were analyzed using probabilistic topic modeling procedures within a machine learning approach. The Latent Dirichlet Allocation method, chosen for modeling, required the following stages: 1) data cleansing, and 2) data modeling into topics for coherence and perplexity analysis. All research was conducted according to the standards of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses in a fully computerized way. The computational literature review is an integral part of a broader literature review process. The results presented met three criteria: (1) literature review for a research area, (2) analysis and classification of journals, and (3) analysis and classification of academic and individual research teams. The contribution of the article is to demonstrate how the publication network is formed in this particular field of research, and how the content of abstracts can be automatically analyzed to provide a set of research topics for quick understanding and application in future projects.

这项研究旨在说明潜在使用概念，技术和挖掘过程工具来改进系统的审查过程。因此，从2012年至2019年，对两个在线数据库（Scopus和ISI Web of Science）进行了审查。总共鉴定出9649项研究，并在机器学习方法中使用概率主题建模程序对这些研究进行了分析。选择进行建模的潜在Dirichlet分配方法需要以下阶段：1）数据清理，以及2）将数据建模为主题以进行一致性和困惑性分析。所有研究均按照完全系统化的系统评价和荟萃分析首选报告项目的标准进行。计算文献审阅是更广泛的文献审阅过程不可或缺的一部分。提出的结果符合三个标准：（1）研究领域的文献综述；（2）期刊的分析和分类；（3）学术和个人研究团队的分析和分类。文章的目的是演示如何在这个特定的研究领域中形成出版网络，以及如何自动分析摘要的内容以提供一组研究主题，以便快速理解并在未来的项目中应用。