As foresight activities continue to increase across multiple arenas and types of organizations, the need to develop effective modes of reviewing future-oriented information against long-term goals and policies becomes more pressing. The activities of institutional sensemaking are vital in constructing potential and desired futures, but remain sensitive to organizational culture and ethos, thus raising concerns about whose futures are being constructed. In viewing foresight studies as a critical component in such sensemaking, this research investigates a method of textual analysis that deploys natural language processing algorithms (NLP). In this research, we introduce and apply the methodology of topic modelling for conducting a comparative analysis to explore how citizen-derived foresight differs from other institutional foresight. Finally we present prospects for further employing NLP for strategic foresight and futures studies.

随着跨多个领域和组织类型的预见性活动不断增加，针对长期目标和政策制定有效的模式来审查面向未来的信息的需求变得越来越紧迫。体制意义上的活动对于构建潜在的和期望的未来至关重要，但是对组织文化和精神保持敏感，因此引起了人们对正在构建的未来的担忧。在将前瞻性研究视为这种意义形成的关键组成部分时，本研究调查了一种采用自然语言处理算法（NLP）的文本分析方法。在这项研究中，我们介绍并应用主题建模的方法进行比较分析，以探索公民衍生的远见与其他机构的远见有何不同。