Systematic literature reviews (SLRs) are essential for researchers to keep up with past and recent research in their domains. However, the rapid growth in knowledge creation and the rising number of publications have made this task increasingly complex and challenging. Moreover, most systematic literature reviews are performed manually, which requires significant effort and creates potential bias. The risk of bias is particularly relevant in the data synthesis task, where researchers interpret each study's evidence and summarize the results. This study uses an experimental approach to explore using machine learning (ML) techniques in the SLR process. Specifically, this study replicates a study that manually performed sentiment analysis for the data synthesis step to determine the polarity (negative or positive) of evidence extracted from studies in the field of agile methodology. This study employs a lexicon‐based approach to sentiment analysis and achieves an accuracy rate of approximately 86.5% in identifying study evidence polarity.

中文翻译：

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使用机器学习进行系统文献综述 案例：敏捷软件开发


系统文献综述 （SLR） 对于研究人员跟上其领域过去和最近的研究至关重要。然而，知识创造的快速增长和出版物数量的增加使这项任务变得越来越复杂和具有挑战性。此外，大多数系统的文献综述都是手动进行的，这需要大量的努力并产生潜在的偏见。偏倚风险在数据合成任务中尤其相关，研究人员解释每项研究的证据并总结结果。本研究使用实验方法探索在 SLR 过程中使用机器学习 （ML） 技术。具体来说，本研究复制了一项研究，该研究为数据合成步骤手动执行情感分析，以确定从敏捷方法领域的研究中提取的证据的极性（消极或积极）。本研究采用基于词典的情感分析方法，在识别研究证据极性方面的准确率约为 86.5%。