Research Trends and Knowledge System Analysis of the Academic Field of Intangible Cultural Heritage through Big Data Text-Mining

This paper aims to analyze the academic big data in the field of intangible cultural heritageusing text-mining and explore the research trends and knowledge system. To achieve concreteresults, the research was conducted with following goals: First, “What is the important centraltheme in the research of intangible cultural heritage?”; Second, “What are the major topics inthe field of intangible cultural heritage research?”; Third, “How the major topics and subjectshave changed in the field of intangible cultural heritage and what are their characteristics?”;and Fourth, “How is the result of analysis visualized into a network map and what are thecharacteristics in it?”With such goals, the research followed 4 steps, ‘Data Collection’, ‘Data Refinement’, ‘DataAnalysis’, and ‘Integrating and Interpretation’. The data was collected during the periodbetween 1965, when the very first paper on intangible cultural heritage was published in SouthKorea, and 2020 from 1,443 academic papers, 1,222 dissertations, and 2,665 abstracts andbibliographic data. The collected unstructured data was refined for computer-aided analysis.Firstly, nominal morphemes were extracted using Korean morpheme analyzer, and then variouscontrolling and TF-IDF analysis were applied. 14,403 words from academic papers and 14,296words from dissertations have undergone topic modeling and text network analysis withNetMiner program.Topic modeling is a probabilistic algorithm to find out subjects and topics hidden in a largeset of documents, and extract and classify documents according to the topic. Text networkanalysis applies the network theories and analysis methods that developed out of sociologyto literature analysis. This method analyzes the structure of connected words in the text andshows the result in the form of a network map. Recent big data analyses is evolving towardsutilizing various optimized analytical techniques in order to enhance the reliability of the analysis result. This paper, thus, used topic modeling and network analysis to draw a result thatis optimal for the purpose of our research.This paper finds its significance in that it contributed to encouragement of relevant studiesas it used the text-mining technique to analyze the big data that has accumulated in the fieldof intangible cultural heritage. In addition, it has a substantial contribution as it provided avisualized knowledge map to reveal the relationship of keywords and main topics in the field ofintangible cultural heritage, which led to intuitive understanding of the abstract contents.