A Textual Analysis of the Musical Wicked Using Text Mining Techniques

Abstract

This study aims to investigate the potential of text mining use to analyze the musical piece Wicked. It delves into the complex concerns and relationships of the characters, uncovering hidden plot patterns. The study shows the main characters, their personalities, and the major themes by using text minig techniques. The findings provide insight into the primary themes, as illustrated by word frequency tools, which can be applied to various fields of education and applied linguistics. The use of text mining will contribute to developing meaningful tagging systems for second and foreign language education, designed to be user-friendly for learners and practitioners alike. Through various textual analytical tools, researchers and educators gain a richer understanding of major themes, advancing academic discourse. Such research will, moreover. Influence educational disciplines and help inform society about the potential of technology in analyzing and interpreting literature and art.

Highlights:

1. The study demonstrates how text mining techniques can be effectively applied to literary text analysis in literary scholarship and educational research.

2. The study quantitatively identifies major characters, themes, and character relationships within the narrative, offering a data-driven alternative to traditional literary interpretation.

3. The findings reveal the central protagonists of the literary work, and the frequent appearance of adjectives also highlights the core themes in the plot.

4. The study uncovers hidden thematic structures and interpersonal conflicts through word association and topic modeling.

5. The research contributes to educational and applied linguistic fields by suggesting that text mining tools can support second and foreign language education, curriculum development, and digital humanities research.