DOI QR코드

DOI QR Code

Using Text Network Analysis for Analyzing Academic Papers in Nursing

간호학 학술논문의 주제 분석을 위한 텍스트네크워크분석방법 활용

  • Received : 2019.02.07
  • Accepted : 2019.04.12
  • Published : 2019.04.30

Abstract

Purpose: This study examined the suitability of using text network analysis (TNA) methodology for topic analysis of academic papers related to nursing. Methods: TNA background theories, software programs, and research processes have been described in this paper. Additionally, the research methodology that applied TNA to the topic analysis of the academic nursing papers was analyzed. Results: As background theories for the study, we explained information theory, word co-occurrence analysis, graph theory, network theory, and social network analysis. The TNA procedure was described as follows: 1) collection of academic articles, 2) text extraction, 3) preprocessing, 4) generation of word co-occurrence matrices, 5) social network analysis, and 6) interpretation and discussion. Conclusion: TNA using author-keywords has several advantages. It can utilize recognized terms such as MeSH headings or terms chosen by professionals, and it saves time and effort. Additionally, the study emphasizes the necessity of developing a sophisticated research design that explores nursing research trends in a multidimensional method by applying TNA methodology.

Keywords

References

  1. Roberts CW, Popping R. Themes, syntax and other necessary steps in the network analysis of texts: a research paper. Soc Sci Info. 1996 Dec 1;35(4):657-65. https://doi.org/10.1177/053901896035004005
  2. Zhang L, Hall M, Bastola D. Utilizing twitter data for analysis of chemotherapy. Int J Med Inform. 2018 Dec;120:92-100. https://doi.org/10.1016/j.ijmedinf.2018.10.002
  3. Park EJ, Kim YJ, Park CS. A comparison of hospice care research topics between Korea and other countries using text network analysis. J Korean Acad Nurs. 2017 Oct;47(5):600-12. https://doi.org/10.4040/jkan.2017.47.5.600
  4. Zavarrone E, Grassia MG. Text analysis: an overview. Wiley StatsRef. 2018. https://doi.org/10.1002/9781118445112.stat08089
  5. Paranyushkin D. [Internet]. Identifying the pathways for meaning circulation using text network analysis. Berlin: Nodus Labs; c2011 [up-dated 2011 Dec 25; cited 2019 Jan 20]. Available from: http://noduslabs.com/research/pathways-meaning-circulation-text-network-analysis
  6. Park CS, Jung JW. Text network analysis: detecting shared meaning through socio-cognitive networks of policy stakeholders. J Gov Studies. 2013 Aug 23;19(2):73-108.
  7. Park EJ, Cho SZ. editors. KoNLPy: Korean natural language processing in python. Proceedings of the 26th Annual Conference on Human & Cognitive Language Technology; 2014 Oct 10-11; Kangwon National University, Chuncheon. 2014 Oct; 10:133-6.
  8. He Q. Knowledge discovery through co-word analysis. Libr Trends. 1999 Summer;48(1):133-59.
  9. Wasserman S, Faust K. Social network analysis: methods and applications. New York: Cambridge University Press; 1994. 825 p.
  10. Baldwin C, Hughes J, Hope T, Jacoby R, Ziebland S. Ethics and dementia: mapping the literature by bibliometric analysis. Int J Geriatr Psychiatry. 2003 Oct 9;18(1):41-54. https://doi.org/10.1002/gps.770
  11. Scott SD, Profetto-McGrath J, Estabrooks CA, Winther C, Wallin L, Lavis JN. Mapping the knowledge utilization field in nursing from 1945 to 2004: a bibliometric analysis. Worldviews Evid Based Nurs. 2010 Jun 14;7(4):226-37. https://doi.org/10.1111/j.1741-6787.2010.00197.x
  12. Lee SK, Jeong S, Kim HG, Yom YH. A social network analysis of research topics in Korean nursing science. J Korean Acad Nurs. 2011 Oct 24;41(5):623-32. https://doi.org/10.4040/jkan.2011.41.5.623
  13. Kim MJ, Noh SM, Ryu EJ, Shin SM. Research trend analysis of do-not-resuscitate decision: based on text network analysis. Asian Onco Nurs. 2014 Dec 24;14(4):254-64. https://doi.org/10.5388/aon.2014.14.4.254
  14. Kwon SY, Park EJ. Knowledge structure of the Korean journal of occupational health nursing through network analysis. Korean J Occup. Health Nurs. 2015 May;24(2):76-85. https://doi.org/10.5807/kjohn.2015.24.2.76
  15. Kwon SY, Bae KR. A study on the knowledge structure of cancer survivors based on social network analysis. J Korean Acad Nurs. 2016 Feb;46(1):50-8. https://doi.org/10.4040/jkan.2016.46.1.50
  16. Kim YJ, Jang SN. Mapping the knowledge structure of frailty in journal articles by text network analysis. PloS one. 2018 Apr 19;13(4):e0196104. https://doi.org/10.1371/journal.pone.0196104
  17. Freeman LC. Centrality in social networks conceptual clarification. Soc Netw. 1978;1:215-39. https://doi.org/10.1016/0378-8733(78)90021-7
  18. Park YS. Formation of nursing knowledge: inductive reasoning. Perspect Nurs Sci. 2004 Dec;1(1):36-49.
  19. Lee SW. Prospects for nursing theory analysis, evaluation and development. Perspect Nurs Sci. 2004 Dec;1(1):1-21.
  20. Lee SS. A content analysis of journal articles using the language network analysis methods. J Korean Soc Info Manag. 2014 Dec ;31(4):49-68. https://doi.org/10.3743/KOSIM.2014.31.4.049
  21. Zhang Y, Chen H, Lu J, Zhang G. Detecting and predicting the topic change of Knowledge-based systems: a topic-based bibliometric analysis from 1991 to 2016. Knowl Based Syst. 2017 Jul 10;133:255-68. https://doi.org/10.1016/j.knosys.2017.07.011
  22. Montemurro MA, Zanette DH. Keywords and co-occurrence patterns in the Voynich manuscript: an information-theoretic analysis. PLoS One. 2013 Jun 21;8(6):e66344. https://doi.org/10.1371/journal.pone.0066344
  23. Lee SS. Network analysis methods. Seoul: Nonhyeong; 2012.370 p.
  24. Borgatti SP, Halgin DS. On network theory. Organ Sci. 2011 Sep;22(5):1168-81. https://doi.org/10.1287/orsc.1100.0641
  25. Park HW, Leydesdorff L. Understanding the KrKwic: a computer program for the analysis of Korean text. J Korean Data Anal Soc. 2004 Oct;6(5):1377-87.
  26. Lee TW, Park KO, Seomun GA, Kim MY, Hwang JI, Yu SY, et al. Analysis of research articles published in the Journal of Korean Academy of Nursing Administration for 3 years (2013-2015): the application of text network analysis. J Korean Acad Nurs Adm. 2017 Jan;23(1):101-10. https://doi.org/10.11111/jkana.2017.23.1.101
  27. Kim SY, Park JE, Seo HJ, Lee YJ, Jang BH, Son HJ, et al. NECA's guidance for undertaking systematic reviews and meta-analyses for intervention. Seoul: National Evidence-based Healthcare Collaborating Agency. 2011. 288 p.
  28. Zheng P, Liang X, Huang G, Liu X. Mapping the field of communication technology research in Asia: content analysis and text mining of SSCI journal articles 1995-2014. Asian J Commun. 2016 Oct 20;26(6):511-31. https://doi.org/10.1080/01292986.2016.1231210
  29. Persson O, Danell R, Schneider JW. How to use Bibexcel for various types of bibliometric analysis. Astrom F, Danell R, Larsen B, Schneider JW, editors. ISSI Newsl [Internet]. 2009 Jun [cited 2019 Jan 20];05-S:9-24. Available from: http://issi-society.org/media/1053/ollepersson60.pdf
  30. Park EJ, Ahn DW, Park CS. Text network analysis of newspaper articles on life-sustaining treatments. J Korean Acad Community Health Nurs. 2018 Jun 1;29(2):244-56. https://doi.org/10.12799/jkachn.2018.29.2.244

Cited by

  1. 간호사의 직장 내 괴롭힘에 대한 국내 연구 동향 분석: 의미연결망분석과 토픽모델링 중심 vol.28, pp.4, 2019, https://doi.org/10.5807/kjohn.2019.28.4.221
  2. Knowledge Structure of Nursing Studies on Heart Failure Patients in South Korea through Text Network Analysis vol.32, pp.4, 2019, https://doi.org/10.7475/kjan.2020.32.4.409
  3. 텍스트마이닝 기법을 활용한 국내 음식관광 연구 동향 분석 vol.35, pp.1, 2020, https://doi.org/10.7318/kjfc/2020.35.1.65
  4. 고에너지 물리학 분야 국내 연구자들의 학술 커뮤니케이션 특성 분석: SCOAP3 오픈 액세스 학술지를 중심으로 vol.37, pp.2, 2019, https://doi.org/10.3743/kosim.2020.37.2.285
  5. Identification of the Knowledge Structure of Cancer Survivors’ Return to Work and Quality of Life: A Text Network Analysis vol.17, pp.24, 2020, https://doi.org/10.3390/ijerph17249368
  6. Trends of Nursing Research on Accidental Falls: A Topic Modeling Analysis vol.18, pp.8, 2019, https://doi.org/10.3390/ijerph18083963
  7. Research trends related to childhood and adolescent cancer survivors in South Korea using word co-occurrence network analysis vol.27, pp.3, 2019, https://doi.org/10.4094/chnr.2021.27.3.201
  8. 당뇨병 모바일 앱 관련 연구동향: 텍스트 네트워크 분석 및 토픽 모델링 vol.23, pp.3, 2019, https://doi.org/10.7586/jkbns.2021.23.3.170
  9. Identifying the Knowledge Structure and Trends of Outreach in Public Health Care: A Text Network Analysis and Topic Modeling vol.18, pp.17, 2019, https://doi.org/10.3390/ijerph18179309