Earticle

초록

영어

In this study, topic modeling technique, which is one of big data analysis techniques using artificial intelligence, was applied to the investigation of the research trends of Quebec literature in North America. The data collection was done through the Web of science, and 421 Quebec literature-related papers published in North America over the last 20 years were collected. The data consisted of the titles, abstracts, and keywords of these papers, and LDA, an algorithm for topic modeling was used to analyze the data. According to the Word Cloud result, it was found that the genres of ‘novel’ and ‘poetry’ were the most studied. As a result of the LDA analysis, eight topics were created, and the topics were : ‘Quebec identity and immigrant litterature’, ‘Short story and essay’, ‘Translation and various cultures’, ‘Quebec novels and authors’, ‘Contemporary Quebec theatre and drama’, ‘Poetry’, ‘History of Quebec literature’, and ‘Quebec women's literature’. The results of this study are significant in that they attempted to analyze a vast amount of literature research papers by applying big data analysis techniques based on artificial intelligence, and are expected to serve as a stepping stone for similar studies in the future.

목차

1. 서론
2. 토픽 모델링과 LDA 기법
3. 선행 연구
4. 연구 방법
4.1 자료 수집
4.2 자료 분석
5. 연구 결과
5.1 워드 클라우드
5.2 주제간 거리 지도 (IDM)
5.3 토픽 분석
6. 결론
인용문헌
[Abstract]

저자

• 배진아 [ Jin Ah Bae | 인하대학교 ] 주저자
• 이준구 [ Jun Goo Lee | 삼성전자 ] 교신저자

참고문헌