Nowadays global supply chains enable companies to enhance competitive advantages, increase manufacturing flexibility and reduce costs through a broader selection of suppliers. Despite these benefits, however, insufficient understanding of uncertain regional differences and changes often increases risks in supply chain operations and even leads to a complete disruption of a supply chain. This paper addresses this issue by proposing a text-mining based global supply chain risk management framework involving two phases. First, the extant literature about global supply chain risks was collected and analyzed using a text-based approaches, including term frequency, correlation, and bi-gram analysis. The results of these analyses revealed whether the term-related content is important in the studied literature, and correlated topic model clustering further assisted in defining potential supply chain risk factors. A risk categorization (hierarchy) containing a total of seven global supply chain risk types and underlying risk factors was developed based on the results. In the second phase, utilizing these risk factors, sentiment analysis was conducted on online news articles, selected according to the specific type of risk, to recognize the pattern of risk variation. The risk hierarchy and sentiment analysis results can improve the understanding of regional global supply chain risks and provide guidance in supplier selection.

如今，全球供应链使公司能够通过选择更多的供应商来增强竞争优势，提高制造灵活性并降低成本。尽管有这些好处，但是，对不确定的地区差异和变化的了解不足通常会增加供应链运营的风险，甚至导致供应链的完全中断。本文通过提出一个基于文本挖掘的全球供应链风险管理框架来解决此问题，该框架涉及两个阶段。首先，使用基于文本的方法收集和分析有关全球供应链风险的现有文献，包括术语频率，相关性和二元语法分析。这些分析的结果表明，术语相关的内容在所研究的文献中是否很重要，相关主题模型聚类进一步帮助定义潜在的供应链风险因素。根据结果​​，制定了包含总共七个全球供应链风险类型和潜在风险因素的风险分类（层次结构）。在第二阶段，利用这些风险因素，对根据特定风险类型选择的在线新闻文章进行了情绪分析，以识别风险变化的模式。风险等级和情绪分析结果可以增进对区域全球供应链风险的了解，并为选择供应商提供指导。在第二阶段，利用这些风险因素，对根据特定风险类型选择的在线新闻文章进行了情绪分析，以识别风险变化的模式。风险等级和情绪分析结果可以增进对区域全球供应链风险的了解，并为选择供应商提供指导。在第二阶段，利用这些风险因素，对根据特定风险类型选择的在线新闻文章进行了情绪分析，以识别风险变化的模式。风险等级和情绪分析结果可以增进对区域全球供应链风险的了解，并为选择供应商提供指导。