Dialogue of machine learning and japan studies: Analysis of journal Japanese studies of institute of Japanese studies, Chinese academy of social sciences (jscass)

Since studies on knowledge areas/knowledge communities are often limited by our labor time, research cost/performance shall hence be prioritized/selected. However, our vision could be enhanced by new interdisciplinary thinking. In this article, we employed the methods of Machine Learning and Natural Language Processing to analyze 2,237 research articles. Those articles were taken from The Journal Japanese Studies published by Institute of Japanese Studies, Chinese Academy of Social Sciences (JSCASS) between 1991 and 2017. Results from the data analysis provided the answers to two questions, namely (1) what fields can be clustered in Japanese Studies, accompanied with their keywords, and (2) the number flows of the fields by year. This study analyzed thousands of Chinese long texts, and captured the key information semi-automatically, in order to efficiently support readers to approach complex information in a simplified manner. This study disclosed information on highly productive writers, highly productive institutes, their annual publications, and the main topics of Japanese Studies, which are Economics, Politics, Management Science, Literature/Ideology, Culture/Society, IR, and Social Economy. It could also provide a macro perspective in the East Asian Studies by new interdisciplinary thinking.