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has been cited by the following article:

• TITLE: Visualization of Special Features in “The Tale of Genji” by Text Mining and Correspondence Analysis with Clustering

  AUTHORS: Hisako Hosoi, Takayuki Yamagata, Yuya Ikarashi, Nobuyuki Fujisawa

  KEYWORDS: Visualization; Scientific Art; The Tale of Genji; Text Mining; Correspondence Analysis; Clustering

  JOURNAL NAME: Journal of Flow Control, Measurement & Visualization, Vol.2 No.1, January 23, 2014

  ABSTRACT: In this paper, visualization of special features in “The Tale of Genji”, which is a typical Japanese classical literature, is studied by text mining the auxiliary verbs and examining the similarity in the sentence style by the correspondence analysis with clustering. The result shows that the text mining error in the number of auxiliary verbs can be as small as 15%. The extracted feature in this study supports the multiple authors of “The Tale of Genji”, which agrees well with the result by Murakami and Imanishi [1]. It is also found that extracted features are robust to the text mining error, which suggests that the classification error is less affected by the text mining error and the possible use of this technique for further statistical study in classical literatures.