About how to make novel Visible by using Newly Translated Tale of Genji as an example
This work addresses the challenge of analyzing classical literature for researchers in digital humanities, though it is incremental as it applies existing NLP methods to a specific text.
The paper tackles the problem of visualizing the narrative structure and emotional content of the Newly Translated Tale of Genji by applying natural language processing techniques like TF-IDF for keyword extraction and emotion analysis, resulting in a network of word relationships and insights into authorship.
This paper aims to make Tales of Genji visible by using natural language processing, mathematic analysis, emiton analysis. Based on novel, mining data from content of this novel at respect of information abstracting. Summing up the fundamental method of novel visualization, our work are as follows: Based on frequency analysis, we use tf-did to abstract keyword of Newly Translated Tale of Genji, which means the most important word in each chapter. We recognize the emotion of word to analysis the emotion of each chapter of Newly Translated Tale of Genji. Next, we think about the connection between the result of emotion analysis and literature analysis, showing we can get same result by natural language processing. We build a network of all the word apperanced in Newly Translated Tale of Genji. Make a study of relationships between words. Further, we search the writer of Uji Chapters.