テキストマイニングを用いた統合報告書の評価モデルの構築法

カテゴリ: 論文誌(論文単位)

グループ名: 【C】電子・情報・システム部門

発行日: 2023/08/01

タイトル(英語): A Construction Method for a Model to Evaluate Integrated Reports using Text Mining

著者名: 田中　良介（筑波大学大学院／（株）岡三証券グループ），津田　和彦（筑波大学大学院）

著者名(英語): Ryohsuke Tanaka (University of Tsukuba/Okasan Securities Group Inc.), Kazuhiko Tsuda (University of Tsukuba)

キーワード: 統合報告書，IRQ，テキストマイニング，機械学習　　integrated report，irq，text mining，machine learning

要約(英語): The purpose of this study is to propose the method for constructing a model to evaluate the quality of the integrated report, called IRQ. The model, which is constructed by the proposed method, is a logistic regression model, that is trained on the descriptions of integrated reports selected and not selected by “Excellent Integrated Reports” of GPIF (Government Pension Investment Fund in Japan). As a result, first, it is confirmed from both quantitative and qualitative perspectives that the IRQ, which is calculated from the model, represents the quality of the integrated report. Second, two of three explanatory variables in the model are statistically significant, indicating that the model captures the descriptive characteristics of the selected integrated reports. Lastly, the model is shown to have a precision of 0.85, which is approximately 0.16 better than precision of a model, that is developed by the method introduced in the previous study. In conclusion, by using the model developed by the proposed method, it is possible to quantitatively clarify the difference in descriptive characteristics between the selected and non-selected integrated reports.

本誌: 電気学会論文誌C（電子・情報・システム部門誌） Vol.143 No.8 （2023） 特集Ⅰ：Smart City を支える高度な情報通信，センシング及び医療技術　特集Ⅱ：情報処理技術の適用による有用情報の獲得と応用

本誌掲載ページ: 793-801 p

原稿種別: 論文／日本語

電子版へのリンク: https://www.jstage.jst.go.jp/article/ieejeiss/143/8/143_793/_article/-char/ja/