Analyzing South Korea’s Defense Challenges with Defense Innovation 4.0 through LDA and BERT Techniques

This paper explores the influence of South Korea’s defense reform initiative, “Defense Innovation 4.0,” by analyzing media coverage before and after its establishment as a national agenda. We collected articles related to defense from a year prior to a year following the announcement. Utilizing the KeyBERT model, we extracted key terms to guide our application of Latent Dirichlet Allocation (LDA) for topic modeling. This approach allowed us to discern the evolving discourse on defense issues, particularly in relation to “Defense Innovation 4.0.” Furthermore, we employed a BERT-based model to assess the content of the “Defense Innovation 4.0” policy against the backdrop of defense discussions pre- and post-announcement. Our findings reveal a notable surge in media attention towards the defense sector, with a significant shift in the thematic focus of private media coverage aligning with the policy’s introduction.

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Publication Details

Journal
IEIE Transactions on Smart Processing and Computing
Published
2025-10-30
DOI
https://doi.org/10.5573/ieiespc.2025.14.5.644
Citations
1
Primary Topic
Computational and Text Analysis Methods
Type
article
Field-Weighted Citation Impact
5.34
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article

Analyzing South Korea’s Defense Challenges with Defense Innovation 4.0 through LDA and BERT Techniques

Doohong Park, Donggoo Kang, Joonki Paik
1 citations
IEIE Transactions on Smart Processing and Computing
Computational and Text Analysis Methods
5.34
article

Analyzing South Korea’s Defense Challenges with Defense Innovation 4.0 through LDA and BERT Techniques

Doohong Park, Donggoo Kang, Joonki Paik
article en
1 citations

Abstract

This paper explores the influence of South Korea’s defense reform initiative, “Defense Innovation 4.0,” by analyzing media coverage before and after its establishment as a national agenda. We collected articles related to defense from a year prior to a year following the announcement. Utilizing the KeyBERT model, we extracted key terms to guide our application of Latent Dirichlet Allocation (LDA) for topic modeling. This approach allowed us to discern the evolving discourse on defense issues, particularly in relation to “Defense Innovation 4.0.” Furthermore, we employed a BERT-based model to assess the content of the “Defense Innovation 4.0” policy against the backdrop of defense discussions pre- and post-announcement. Our findings reveal a notable surge in media attention towards the defense sector, with a significant shift in the thematic focus of private media coverage aligning with the policy’s introduction.

IEIE Transactions on Smart Processing and ComputingVol. 14(5)
Openalex Percentile: Top 3%
Computational and Text Analysis Methods
5.34
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