Using Text Network Analysis for Analyzing Academic Papers in Nursing

Chan Sook Park

doi:10.16952/pns.2019.16.1.12

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Park: Using Text Network Analysis for Analyzing Academic Papers in Nursing

Original Article

Perspectives in Nursing Science 2019; 16(1): 12-24.

Published online: 30 April 2019

DOI: https://doi.org/10.16952/pns.2019.16.1.12

Using Text Network Analysis for Analyzing Academic Papers in Nursing

Chan Sook Park

Instructor, Department of Nursing, Konkuk University, Chungju, Korea.

Corresponding author: Park, Chan Sook. Department of Nursing, Konkuk University, 268 Chungwon-daero Chungju 27478, Korea. Tel: +82-43-840-3953, Fax: +82-43-840-3958, sook706@hanmail.net

Received 7 February 2019 Revised 11 April 2019 Accepted 12 April 2019

The Research Institute of Nursing Science Seoul National University

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: Text mining, Nursing methodology research, Social networking

Figures and Tables

Fig. 1

The study flow of text-network analysis.

Fi. 2

An example of co-occurrence matrix.

Fig. 3A

An example of word cloud.

Fig. 3B

An example of sociogram.

Table 1

Korean Researches applying Text Network Analysis in Nursing Discipline

COOC=Co-occurrence Matrix Generation Program; GN=Girvan & Newman; KrKwic=Korean keyword in context; NA=not applicable; PFNet=Pathfinder network.

Table 2

An Example of Keyword Ranks based on Freeman's Centrality

PCA=Patient controlled analgesia; QOL=Quality of life; RNs=Registered nurses.

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