earticle

영어

In this paper, as one of the attempts to effectively utilize the vast amount of text data, I have introduced a text mining technique called Topic Model into the field of Japanese studies. Concretely, the texts of the reading comprehension parts of the previous format JLPT for the past 20 years were collected, and Topic Model analysis was carried out. The following points were made clear by such a study. First of all, it was confirmed from actual data that the subjects of the previous format JLPT tried to avoid topic-specific biases when selecting and producing the texts for the questions. Next, the text can be statistically classified into four main topics: “Private relationships such as family and work,” “Communications related to schedules,” “Public relations related to the country and society,” and “Economic activity.” The techniques and results of topic model analysis in this paper were empirical analyzes of actual existing questions. It is considered significant in that it can be applied to all fields of Japanese studies that are needed. Of course, the discussion in this paper is limited to the texts of the previous format JLPT, not the new format JLPT, and the amount of data is relatively small, although it covers all the data for the past 20 years. In addition, a comparative analysis with other texts was not possible. Therefore, it seems that there is still room for improvement in this paper, but I would like to address this as a future issue.