Subjects: Library Science,Information Science >> Information Science submitted time 2023-04-01 Cooperative journals: 《图书情报工作》
Abstract: [Purpose/significance] Topic evolution analysis plays an important role in detection the technology frontier detection and innovation strategy deployment. [Method/process] In this paper, the topic evolution analysis process was divided into several steps: topic representation, similarity correlation and intensity evolution calculation. The LDA model was used to represent the topic; content, co-occurrence, and trend similarity were proposed for topic correlation calculations, and the prophet-based pre-train fine-tuning model was used to predict the topic trends. An empirical analysis was conducted using the stem cell field as an example. [Result/conclusion] Experiments show that the Logistic growth model has a R2Score of more than 0.90 for each topic. It shows that the Logistic growth model in Prophet is consistent with the growth trend of topics, and can fit the evolution trend of the topic intensity. The topic evolution model proposed in this paper has certain reference to topic distribution and evolution analysis in specific fields.