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.
Highlights
The scientific arts have been a topic of interests in recent study of visualization
The purpose of this paper is to develop an efficient method of part-of-speech classification using text mining, and the feature extraction of “The Tale of Genji” is carried out by using the statistical correspondence analysis combined with clustering
The results indicate that most of the chapters in “Murasakinoue” are located closer together, while those of “Tamakazura” are placed in the other cluster, which indicates the difference in sentence style of the “Murasakinoue” and “Tamakazura”
Summary
The scientific arts have been a topic of interests in recent study of visualization. This research field has been studied by applying the visualization technique developed in the field of science and engineering to the field of liberal arts, such as literature, social science, education, archaeology and others. The application of the visualization technique to the field of literature is becoming active in recent years. Inami et al [10] introduced discrete wavelet multi-resolution analysis into “The Tale of Genji”, and they successfully visualized the variations of emotional feeling stream of the major characters in the story along with the story progression. Yamada and Murai [11,12] developed a story-visualization technique by color coding the key-words and applying the interpolation technique using Laplace equation to understand the time variation of emotional feeling in the Shakespeare’s play. Carpena et al [13] visualizes spectra of the words frequency in “Don Quixote” based on statistical analysis
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