논문 서지정보를 이용한 빈산소수괴 연구 분야의 연구용어 빈도분석
Frequency Analysis of Scientific Texts on the Hypoxia Using Bibliographic Data
1
조회
0
다운로드
1
인용
0
참고문헌
키워드
초록
The frequency analysis of scientific terms using bibliographic information is a simple concept, but as relevant data become more widespread, manual analysis of all data is practically impossible or only possible to a very limited extent. In addition, as the scale of oceanographic research has expanded to become much more comprehensive and widespread, the allocation of research resources on various topics has become an important issue. In this study, the frequency analysis of scientific terms was performed using text mining. The data used in the analysis is a general-purpose scholarship database, totaling 2,878 articles. Hypoxia, which is an important issue in the marine environment, was selected as a research field and the frequencies of related words were analyzed. The most frequently used words were ‘Organic matter’, ‘Bottom water’, and ‘Dead zone’ and specific areas showed high frequency. The results of this research can be used as a basis for the allocation of research resources to the frequency of use of related terms in specific fields when planning a large research project represented by single word.
Abstract
The frequency analysis of scientific terms using bibliographic information is a simple concept, but as relevant data become more widespread, manual analysis of all data is practically impossible or only possible to a very limited extent. In addition, as the scale of oceanographic research has expanded to become much more comprehensive and widespread, the allocation of research resources on various topics has become an important issue. In this study, the frequency analysis of scientific terms was performed using text mining. The data used in the analysis is a general-purpose scholarship database, totaling 2,878 articles. Hypoxia, which is an important issue in the marine environment, was selected as a research field and the frequencies of related words were analyzed. The most frequently used words were ‘Organic matter’, ‘Bottom water’, and ‘Dead zone’ and specific areas showed high frequency. The results of this research can be used as a basis for the allocation of research resources to the frequency of use of related terms in specific fields when planning a large research project represented by single word.
참고문헌 (35)
- 이기섭, "해안해양공학 연구 분야의 SCOPUS 서지정보 Text Mining 분석", 한국해안·해양공학회논문집, 30(1), pp.19~28 (2018), 한국해안,해양공학회[DOI]
- 정용복, "텍스트 마이닝을 이용한 암반공학분야 SCI논문의 주제어 분석", 터널과 지하공간, 25(4), pp.303~319 (2015), 한국암반공학회
- 조근호, "텍스트 마이닝을 이용한 산업공학 연구기법의 분석", 대한산업공학회지, 40(1), pp.52~59 (2014), 대한산업공학회[DOI]
- 조수곤, "텍스트마이닝을 활용한 산업공학 학술지의 논문 주제어간 연관관계 연구", 대한산업공학회지, 38(1), pp.67~73 (2012), 대한산업공학회
- Aria M, "Bliometrix: an R-tool for comprehensive science mapping analysis", J Informetr, 11(4), pp.959~975 (2017)
- Bartlett MS, "The use of transformations", Biometrics, 3(1), pp.39~52 (1947)
- Boussalis C, "Text-mining the signals of climate change doubt", Global Environ Chang, 36, pp.89~100 (2016)
- Costa S, "Bibliometric analysis of ocean literacy: an underrated term in the scientific literature", Mar Policy, 87, pp.149~157 (2018)
- "Wordcloud2: create word cloud by ‘htmlwidget’. R package version 0.2.1" (2018)[URL]
- Diaz RJ, "Spreading dead zones and consequences for marine ecosystems", Science, 321(5891), pp.926~929 (2008)
- Donoho D, "50 years of data science", J Comput Graph Stat, 26(4), pp.745~766 (2017)
- Gattuso JP, "Patterns of publication effort in coastal biogeochemistry: a bibliometric survey (1971 to 2003)", Mar Ecol-Prog Ser, 294, pp.9~22 (2005)
- Gavish M, "Three dream applications of verifiable computational results", Comput Sci Eng, 14(4), pp.26~31 (2012)
- Hui I, "Shaping the coast with permits: making the state regulatory permitting process transparent with text mining", Coast Manage, 45(3), pp.179~198 (2017)
- Keeling RF, "Ocean deoxygenation in a warming world", Annu Rrev Mar Sci, 2, pp.199~229 (2010)
- Landhuis E, "Scientific literature: information overload", Nature, 535(7612), pp.457~458 (2016)
- Meyer D, "Text mining infrastructure in R", J Stat Softw, 25(5), pp.1~54 (2008)
- Ming F, "Stock market prediction from WSJ: text mining via sparse matrix factorization", IEEE International Conference on Data Mining (2014)
- Oschlies A, "Drivers and mechanisms of ocean deoxygenation", Nat Geosci, 11(7), pp.467~473 (2018)
- "Estimating normalization transformations with bestNormalize" (2017)[URL]
- Pita P, "A matter of scales:does the management of marine recreational fisheries follow the ecosystem approach to fisheries in Europe?", Mar Policy, 97, pp.61~71 (2018)
- Robertson S, "Understanding inverse document frequency:on theoretical arguments for IDF", J Doc, 60(5), pp.503~520 (2004)
- Murray A. Rudd, "What a decade (2006−15) of journal abstracts can tell us about trends in ocean and coastal sustainability challenges and solutions", Frontiers in Marine Science, 4, pp.170~ (2017), Frontiers Media SA[DOI]
- "Pluralize: pluralize and singularize any (English) word, R package version 0.1. 0" (2016)[URL]
- Julia Silge, "tidytext: Text Mining and Analysis Using Tidy Data Principles in R", The Journal of Open Source Software, 1(3), pp.37~ (2016), The Open Journal[DOI]
- Sparck JK, "A statistical interpretation of term specificity and its application in retrieval", J Doc, 28(1), pp.11~21 (1972)
- Sun J, "A historical review and bibliometric analysis of research on estuary pollution", Mar Pollut Bull, 64(1), pp.13~21 (2012)
- Tapio I, "Taxon abundance, diversity, co-occurrence and network analysis of the ruminal microbiota in response to dietary changes in dairy cows", PLoS One, 12(7), pp.e0180260~ (2017)
- Van der Waerden BL, "Order tests for the two-sample problem and their power", Indagat Math, 55, pp.453~458 (1952)
- Van Driel MA, "A text-mining analysis of the human phenome", Eur J Hum Genet, 14(5), pp.535~542 (2006)
- David Westergaard, "A comprehensive and quantitative comparison of text-mining in 15 million full-text articles versus their corresponding abstracts", PLOS Computational Biology, 14(2), pp.e1005962~ (2018), Public Library of Science (PLoS)[DOI]
- "Tidyverse: easily install and load the ‘Tidyverse’. R package version 1.2.1" (2017)[URL]
- Wu Y, "Using social media to strengthen public awareness of wildlife conservation", Ocean Coast Manage, 153, pp.76~83 (2018)
- Yang D, "History and trends in solar irradiance and PV power forecasting: a preliminary assessment and review using text mining", Sol Energy, 168, pp.60~101 (2018)
- Zhang W, "A bibliometric analysis of research related to ocean circulation", Scientometrics, 80(2), pp.305~316 (2009)