This study aims to examine research trends related to the bodies of contemporary dance maors in South Korea and explore main research topics on body. As a research method, a text mining techniques, one of big data methods, was used to analyze the trends. As a result, it was found that the frequency of the term 'influence' was the highest, and N-gram connections related to 'influence,' 'dance,' and 'injury' were among the top findings. Topic modeling revealed themes such as 'dancers' body management,' 'emotional changes according to physical condition,' and 'the correlation between eating behavior and emotions in dance.' These results are expected to provide foundational data and direction for subsequent research.

The purpose of this study is to develop interdisciplinary research between dance studies and big data analysis. To this end, the text mining technique, which extracts meaningful information from text, was adopted as the research methodology. In the process of text mining, original PDF texts on the themes of Chum/Muyong(dance), morphological analysis, user dictionary construction, and social network analysis were collected to extract significant named entities and clarify the relations between them. The outcomes of the process, which comprised the extracted text data (total 10,231 copies), a named entity classification table, and a network of named entities, were loaded into the big data analysis system under development. The findings of the study are as follows: First, there were 25 total morpheme types, with 24,691 words with a frequency of more than 100. From these, a second morphemic analysis of sentences containing words such as “Chum” (춤), “Mu” (무), and “dance” (댄스) was conducted. It was revealed that in parts of speech with a frequency of 10 or more, there were 3,057 nouns, 602 proper nouns, 352 verbs, 205 numbers, 135 adjectives, and 35 adverbs. Second, a user dictionary was developed in the form of a taxonomy with stratification between hyperonym and hyponyms. The dictionary contained 2,404 words, which were classified by theme, person, dance piece, genre, theory, function, element, and period. Third, social network analysis revealed that the terms “Muyong,” “Chum,” and “arts and culture” were closely interconnected at the heart of the network. In contrast, dance deviated somewhat from the center. “Dance” was the only word to be connected with the network of dance sports and jazz. This study is significant because it represents the first attempt to apply text mining to written records on dance. In addition, it could suggest ways to expand the use of big data analysis to dance studies. Based on the study, a big data analysis system that is specialized in dance was developed, and the contents will be updated continuously.