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Bolin HUA
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Computer Science & IT

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BERTopic-Driven Identification of Emerging Technology Topics: A Multi-Source Framework with Empirical Validation in New Energy Vehicles Dakun Wang; Bolin HUA
Journal of Systems Engineering and Information Technology (JOSEIT) Vol 4 No 2 (2025): September 2025
Publisher : Ikatan Ahli Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/joseit.v4i2.7169

Abstract

This study addresses the critical challenge of accurately detecting emerging technology topics from large-scale heterogeneous data sources. The methodology encompasses four sequential stages: (1) multi-source data acquisition from academic papers, patents, policies, and technical reports; (2) BERTopic-based topic modeling utilizing BERT embeddings and c-TF-IDF for enhanced semantic representation; (3) topic consolidation through cosine similarity analysis of topic vectors; and (4) emerging topic identification via a weighted evaluation system incorporating novelty, growth, continuity, and impact dimensions. Applied to the new energy vehicle domain using data from 2010-2022, the framework successfully identified 16 candidate emerging technology topics through analysis of 27,058 academic papers and 54,572 patents. Validation results indicate that 12 of the 16 identified topics (75% accuracy) align with technological priorities outlined in government policies and industry reports. The method effectively captures cross-domain technological convergence, with four common topics identified between academic and patent datasets, primarily concentrated in battery technology domains.
Co-Authors Dakun Wang