Earticle

초록

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.

목차

1. はじめに
2. 先行研究
2.1 テキストマイニング(Text Mining)
2.2 トピックモデル(Topic Model)
3. 研究方法
3.1 データの収集
3.2 データの前処理
3.3 データの分析
4. 分析結果
4.1. LDAにおけるトピックの数の設定
4.2. LDAによるトピックモデルリング
4.3. トピック分析
5. おわりに
参考文献

키워드

저자

• 金曘泳 [ 김유영 | 同徳女子大学 日本語学科 副教授 ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  일본언어문화 [Journal of japanese Language and Culture]

• 간기

  계간

• pISSN

  1598-9585

• 수록기간

  2002~2025

• 등재여부

  KCI 등재

• 십진분류

  KDC 730 DDC 495