토픽 모델링을 활용한 북미의 퀘벡문학 연구동향 분석

초록

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.

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