Text Mining of Wood Science Research Published in Korean and Japanese Journals

• Published in: 목재공학 Vol. 51; no. 6; pp. 458 - 469
• Main Author: Eun-suk Jang
• Format: Journal Article
• Language: Korean
• Published: 한국목재공학회 30.11.2023
• Subjects: co-occurrence network analysis; Journal of the Korean Wood Science and Technology; Journal of Wood Science; research information; term frequency-inverse document frequency (TF-IDF); text mining; word frequency analysis; term frequency-inverse document frequency (TF-IDF); word frequency analysis; text mining; co-occurrence network analysis; research information; Journal of the Korean Wood Science and Technology; Journal of Wood Science
• ISSN: 1017-0715
• DOI: 10.5658/WOOD.2023.51.6.458

Text mining techniques provide valuable insights into research information across various fields. In this study, text mining was used to identify research trends in wood science from 2012 to 2022, with a focus on representative journals published in Korea and Japan. Abstracts from Journal of the Korean Wood Science and Technology (JKWST, 785 articles) and Journal of Wood Science (JWS, 812 articles) obtained from the SCOPUS database were analyzed in terms of the word frequency (specifically, term frequency-inverse document frequency) and co-occurrence network analysis. Both journals showed a significant occurrence of words related to the physical and mechanical properties of wood. Furthermore, words related to wood species native to each country and their respective timber industries frequently appeared in both journals. CLT was a common keyword in engineering wood materials in Korea and Japan. In addition, the keywords “MDF,” “MUF,” and “GFRP” were ranked in the top 50 in Korea. Research on wood anatomy was inferred to be more active in Japan than in Korea. Co-occurrence network analysis showed that words related to the physical and structural characteristics of wood were organically related to wood materials.