An alternative topic model based on Common Interest Authors for topic evolution analysis

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 (CIA11CIA: Common Interest Authors), 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.