인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
크롬 서지관리 다운로드
지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
피인용 0
초록·키워드
This study quantitatively analyzes scene-level semantic similarity in five short animation scenarios generated by generative artificial intelligence (ChatGPT-4.0). The primary aim is to verify the structural differentiation between scenarios and their internal cohesion, while also interpreting these features from an integrated perspective. Using TF-IDF and cosine similarity, an inter-scenario distance matrix was constructed, revealing that Scenario ID 3 formed an independently differentiated narrative structure, whereas IDs 1, 2, 4, and 5 clustered within a narrower semantic range.
The analysis revealed distinctive characteristics across scenarios and cohesion within each scenario. Some showed independent structural patterns, while most followed traditional narrative forms, indicating that AI-generated narratives possess a certain degree of structural completeness rather than arbitrary outcomes. Unlike previous research, which has focused primarily on qualitative evaluations, this study offers empirical verification of narrative structure through quantitative analysis.
Furthermore, this research combines scientific text-mining techniques with the artistic context of animation scenarios, suggesting new possibilities for interdisciplinary studies between engineering and the arts. Despite some limitations, this study demonstrates the structural patterns of AI narratives and highlights their creative potential for academic and practical applications. Future research will require a more integrated approach combining large-scale data and advanced methods.
The analysis revealed distinctive characteristics across scenarios and cohesion within each scenario. Some showed independent structural patterns, while most followed traditional narrative forms, indicating that AI-generated narratives possess a certain degree of structural completeness rather than arbitrary outcomes. Unlike previous research, which has focused primarily on qualitative evaluations, this study offers empirical verification of narrative structure through quantitative analysis.
Furthermore, this research combines scientific text-mining techniques with the artistic context of animation scenarios, suggesting new possibilities for interdisciplinary studies between engineering and the arts. Despite some limitations, this study demonstrates the structural patterns of AI narratives and highlights their creative potential for academic and practical applications. Future research will require a more integrated approach combining large-scale data and advanced methods.
본문·목차
인공지능 문자 인식 모델을 통해 추출된 텍스트로, 일부 오타나 오류가 포함될 수 있으나 지속적으로 개선 중입니다.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
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UCI(KEPA) : I410-151-26-02-095622089
