Scholars conduct systematic literature reviews to summarize knowledge and identify gaps in understanding. Machine learning can assist researchers in carrying out these studies. This paper introduces a machine learning toolkit that employs Network Analysis and Natural Language Processing methods to extract textual features and categorize academic papers. The toolkit comprises two algorithms that enable researchers to: (a) select relevant studies for a given theme; and (b) identify the main topics within that theme. We demonstrate the effectiveness of our toolkit by analyzing three streams of literature: cobranding, coopetition, and the psychological resilience of entrepreneurs. By comparing the results obtained through our toolkit with previously published literature reviews, we highlight its advantages in enhancing transparency, coherence, and comprehensiveness in literature reviews. We also provide quantitative evidence about the toolkit's efficacy in addressing the challenges inherent in conducting a literature review, as compared with state-of-the-art Natural Language Processing methods. Finally, we discuss the critical role of researchers in implementing and overseeing a literature review aided by our toolkit.

中文翻译：

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用于在系统文献综述中选择研究和主题的机器学习工具包

学者们进行系统的文献综述，以总结知识并找出理解中的差距。机器学习可以帮助研究人员进行这些研究。本文介绍了一个机器学习工具包，该工具包采用网络分析和自然语言处理方法来提取文本特征并对学术论文进行分类。该工具包包括两种算法，使研究人员能够： （a） 为给定主题选择相关研究;以及 （b） 确定该主题中的主要主题。我们通过分析三个文献流来证明我们工具包的有效性：联合品牌、合作竞争和企业家的心理弹性。通过将通过我们的工具包获得的结果与以前发表的文献综述进行比较，我们强调了它在提高文献综述的透明度、连贯性和全面性方面的优势。我们还提供了定量证据，说明与最先进的自然语言处理方法相比，该工具包在解决进行文献综述所固有挑战方面的有效性。最后，我们讨论了研究人员在我们的工具包辅助下实施和监督文献综述的关键作用。