Earticle

초록

영어

The purpose of this study is to conduct a BERTopic analysis of abstracts from papers on generative artificial intelligence indexed in SCOPUS to identify the semantic structure of research topics and their major application areas. Generative AI has evolved into a technology capable of generating new content such as text, images, code, and speech, and its scope of research has recently been rapidly expanding into fields such as education, healthcare, business, marketing, law and regulation, and information security. However, existing bibliometric analyses often focus on keyword frequency and co-occurrence relationships, which limits their ability to identify latent topics based on the contextual meaning contained in paper abstracts and to comprehensively describe the structure among topics. Therefore, this study selected abstracts of generative AI-related papers collected from SCOPUS as the subject of analysis. We converted the preprocessed abstracts into document embeddings and applied a procedure consisting of dimension reduction using UMAP, density-based clustering using HDBSCAN, and the calculation of topic representative terms using c-TF-IDF. The analysis yielded a total of 22 topics, which were reorganized into eight higher-level research domains: Education and Knowledge Services, Creativity and Content, Healthcare and Science, LLM and Technical Infrastructure, Management and Operations, Marketing and Services, Trust and Governance, and Cybersecurity. The topic similarity and hierarchical clustering results demonstrated that generative AI research is diversifying into research that supports human knowledge activities, research that supports decision-making in organizations and industries, and research that manages technological infrastructure and social risks.

목차

Abstract
I. INTRODUC
2. THEORETICAL BACKGROUND
2.1. Generative AI
2.2. Research Trends in Generative AI
2.3. Topic Modeling
2.4. BERTopic
3. RESEARCH DESIGN AND PROCEDURES
3.1. Research Subjects
3.2. Data Preprocessing
3.3. BERTopic Analysis Model
3.4. Analysis Procedure
4. RESEARCH RESU
4.1. Topic Identification Results
4.2. Word Score Analysis
4.3. Similarity Matrix
4.4. Hierarchical Clustering
4.5. Distance Between Topics
4.6. Topic Probability Distributions
4.7. Higher-Level Research Areas
5. CONCLUSION
References

키워드

저자

• Guozhong Li [ Department of MSIS, Kunming University of Science and Technology, Yunnam, China. ]
• Seongtaek Park [ Department of MIS, Chungbuk National University, Chungbuk, South Korea. ] Corresponding Author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Intelligent Technologies and Innovative Practices

• 간기

  계간

• eISSN

  3092-412X

• 수록기간

  2026~2026

• 십진분류

  KDC 323 DDC 338