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Topic Classification of Public Sector Accounting Research: A BERT-Based Approach

Makoto Kuroki & Yoshitaka Hirose

Journal of Emerging Technologies in Accounting2025https://doi.org/10.2308/jeta-2024-019article
AJG 1ABDC B
Weight
0.50

Abstract

This study applies BERT-based topic modeling (BERTopic) to analyze 306 public sector accounting research (PSAR) papers published between 1980 and 2019 in 11 leading accounting journals. PSAR is an ideal setting for exploring the potential of BERTopic owing to its interdisciplinary nature, combining quantitative and qualitative elements, and its relevance to addressing complex public sector challenges. This study addresses two research questions: (RQ1) What are the main PSAR topics identified by BERT-based modeling? (RQ2) What future PSAR research agendas can be proposed using BERT-based modeling? The findings show the effectiveness of BERTopic in identifying the main topics of PSAR over the 40-year period of analysis, suggesting opportunities for expanding PSAR through advanced topic modeling techniques. Furthermore, the findings can guide accounting researchers in exploring underrepresented topics and advancing the integration of technology across accounting domains. Data Availability: The list of papers that support the findings of this study are available from the first author upon request.

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https://doi.org/https://doi.org/10.2308/jeta-2024-019

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@article{makoto2025,
  title        = {{Topic Classification of Public Sector Accounting Research: A BERT-Based Approach}},
  author       = {Makoto Kuroki & Yoshitaka Hirose},
  journal      = {Journal of Emerging Technologies in Accounting},
  year         = {2025},
  doi          = {https://doi.org/https://doi.org/10.2308/jeta-2024-019},
}

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Topic Classification of Public Sector Accounting Research: A BERT-Based Approach

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.