Topic modeling methods aim to extract semantic topics from unstructured documents, and topic evolution is one of its branches seeking to analyze how temporal topics in a set of documents evolve and has shown successful identification of content transitions within static topics over time; yet, the inherent limitations of topic modeling methods inhibit traditional topic evolution methods from highlighting topical correlations between different, dynamic topics. The authors propose an alternative topic modeling method conscious of the topical correlation in the academic domain by introducing the notion of the common interest authors (CIA¹), defining a topic as a set of shared common research interests of a researcher group. Publication records related to the Human Computer Interaction field were extracted from the Microsoft Academic Graph dataset, with virtual reality as the target field of research. The result showed that the proposed alternative topic modeling is capable of successfully model coherent topics regardless of the topic size with only the meta-data of the document set, indicating that the alternative approach is not only capable of allowing topic correlation analysis during the topic evolution but also able to generate coherent topics at the same time.

主题建模方法旨在从非结构化文档中提取语义主题，而主题演化是其分支机构之一，旨在分析一组文档中的时间主题如何演化，并已显示出随着时间的推移成功识别静态主题内的内容转换。然而，主题建模方法的固有局限性阻止了传统主题演化方法强调不同动态主题之间的主题相关性。作者通过引入共同兴趣作者（CIA ¹的概念）提出了一种替代性的主题建模方法，以了解学术领域中的主题相关性。），将一个主题定义为一组研究人员共享的共同研究兴趣。从Microsoft Academic Graph数据集中提取了与人机交互领域相关的出版物记录，并将虚拟现实作为研究的目标领域。结果表明，无论主题大小如何，仅使用文档集的元数据，所提出的替代主题建模方法都能成功地对相关主题进行建模，这表明替代方法不仅能够在主题演化过程中进行主题相关性分析而且还可以同时产生连贯的主题。