Topic modelling approach is widely used for text data mining in NLP(Natural Language Processing). Text mining has been used for analysis of ICH (intangible cultural heritage), where Cantonese opera is a representative ICH of Lingnan culture. This study retrieved news content on Cantonese Opera and used machine learning analysis (LDA topic modelling) method to find out the distribution of the topics. Four main themes are concluded: the development, cooperation, and inheritance of Cantonese opera(taken up to 45.1% in all data); The traditional form(23.5%); Innovative forms(18.3%); Education and cultural inheritance of Cantonese opera(13.2%). This research further explored how to better promote Cantonese opera by analysing the topics as well as the data, and suggested that emphasis should be placed on the innovation of traditional elements in Cantonese opera, keeping them close to life, and education.
KEYWORDS: Video, Web 2.0 technologies, Cultural heritage, Data mining, Analytical research, Gold, Video processing, Data processing, Data modeling, Visualization
This paper intends to improve the dissemination of Chinese Chaozhou woodcarving by text mining analysis with users’ comments on TikTok. By LDA topic modeling analysis, we found the main themes users discuss are about: 1. Chaozhou woodcarving skills and aesthetics. 2. Chaozhou local cultural recognition and self-confidence. 3. Inheritance and continuation of Chaozhou woodcarving. Based on the analysis of user discussions on World Intangible Cultural Heritage, with Chaozhou woodcarving as an example, we suggested that in order to improve the dissemination, we need to: highlight the regional characteristics, arouse the hometown feelings and cultural resonance of young people in the Chaozhou area, add more practical elements to the work, and let it develop from art works to practical items.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.