In this paper, we focus on the question to what extent machine learning (ML) tools can be used to support systematic literature reviews. We apply a ML approach for topic detection to analyze emerging topics in the literature—our context is accounting and finance research in the Asia–Pacific region. To evaluate the robustness of the approach, we compare findings from the automated ML approach with the results from a manual analysis of the literature. The automated approach uses a keyword algorithm detection mechanism whereby the manual analysis uses common techniques for qualitative data analysis, that is, triangulation between researchers (expert judgement). From our paper, we conclude that both methods have strengths and weaknesses. The automated analysis works well for large corpora of text and provides a very standardized and non‐biased way of analyzing the literature. However, the human researcher is potentially better equipped to evaluate current issues and future trends in the literature. Overall, the best results might be achieved when a variety of tools are used together. [ABSTRACT FROM AUTHOR]

中文翻译：

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机器学习和专家判断：分析亚太地区会计和金融研究的新兴主题

在本文中，我们关注的问题是机器学习 (ML) 工具可以在多大程度上用于支持系统的文献综述。我们应用 ML 方法进行主题检测来分析文献中的新兴主题——我们的背景是亚太地区的会计和金融研究。为了评估该方法的稳健性，我们将自动 ML 方法的结果与文献手动分析的结果进行比较。自动化方法使用关键字算法检测机制，手动分析使用常用技术进行定性数据分析，即研究人员之间的三角测量（专家判断）。从我们的论文中，我们得出结论，这两种方法都有优点和缺点。自动分析适用于大型文本语料库，并提供了一种非常标准化和无偏见的文献分析方式。然而，人类研究人员可能更有能力评估文献中的当前问题和未来趋势。总的来说，当多种工具一起使用时，可能会获得最好的结果。[作者摘要]