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Scientific Information Research

Keywords

topic evolution; BERTopic; semantic function; topic identification

Abstract

[Purpose/significance] Topic evolution analysis can help researchers quickly grasp the research hotspots and development trends of a discipline. However, existing topic models often overlook the semantic functions and structures of texts during topic extraction, making it difficult to reveal the deeper patterns of disciplinary development. This paper proposes an integrated framework for topic evolution analysis that combines the BERTopic model with semantic functions, aiming to enrich and improve the methodological system of topic evolution research.

[Method/process] Firstly, the BERTopic model is used to extract topics, obtaining the“Topic-Word”distribution. Next, a discourse parsing tool analyzes abstracts into five semantic function segments, resulting in the “Semantic Function-Word” distribution. Finally, the two distributions are mapped to obtain the “Topic-Semantic Function” distribution. This approach analyzes topics from a semantic function perspective and explores the impact of semantic function distribution on topic evolution.

[Result/conclusion]An empirical study in the field of library and information science shows that the semantic function distribution of a topic affects its research popularity. Topics oriented towards “Method” and “Objective” may continue to rise in the future, while topics oriented towards “Background” are relatively mature and may enter a decline phase. The proposed method provides a more granular and accurate analysis of discipline development dynamics, helping the academic community better understand the dynamic changes in research hotspots.

First Page

13

Last Page

23

Digital Object Identifier (DOI)

ꎺ 10.19809/j.cnki.kjqbyj.2025.04.002

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