In this research various concepts from network theory and topic modelling are combined, to provision a temporal network of associated topics. This solution is presented as a step-by-step process to facilitate the evaluation of latent topics from unstructured text, as well as the domain area that textual documents are sourced from. In addition to ensuring shifts and changes in the structural properties of a given corpus are visible, non-stationary classes of cooccurring topics are determined, and trends in topic prevalence, positioning, and association patterns are evaluated over time. The aforementioned capabilities extend the insights fostered from stand-alone topic modelling outputs, by ensuring latent topics are not only identified and summarized, but more systematically interpreted, analysed, and explained, in a transparent and reliable way.

在这项研究中，来自网络理论和主题建模的各种概念相结合，以提供相关主题的时间网络。该解决方案以循序渐进的方式呈现，以促进从非结构化文本以及文本文档来源的领域区域评估潜在主题。除了确保给定语料库的结构属性的变化和变化是可见的，还确定了共现主题的非平稳类，并且随着时间的推移评估了主题流行、定位和关联模式的趋势。上述功能通过确保潜在主题不仅被识别和总结，而且以透明和可靠的方式更系统地解释、分析和解释，从而扩展了从独立主题建模输出中产生的见解。