A qualitative analysis framework using natural language processing and graph theory
Open accessAbstract
This paper introduces a method of extending natural language-based processing of qualitative data analysis with the use of a very quantitative tool—graph theory. It is not an attempt to convert qualitative research to a positivist approach with a mathematical black box, nor is it a “graphical solution”. Rather, it is a method to help qualitative researchers, especially those with limited experience, to discover and tease out what lies within the data. A quick review of coding is followed by basic explanations of natural language processing, artificial intelligence, and graph theory to help with understanding the method. The process described herein is limited by neither the size of the data set nor the domain in which it is applied. It has the potential to substantially reduce the amount of time required to analyze qualitative data and to assist in the discovery of themes that might not have otherwise been detected.
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Natural language processing provided a means to diagnose the problem and make a recommendation by simplifying and accelerating the discovery of what lies within the data.
“Natural language processing (NLP) provided a means to diagnose the problem and make a recommendation by simplifying and accelerating the discovery of what lies within the data [22].”
From A Review of Artificial Intelligence (AI) in Education from 2010 to 2020 (2021) · cites this paper as [22]
The Abstract (page not available) says the NLP-and-graph-theory method can help researchers discover what lies within qualitative data and potentially reduce analysis time. Categories Developed From Study Data (page not available) reports recommendations drawn from the resulting themes. The paper does not describe NLP itself as diagnosing a problem, and it attributes theme discovery to a combined method rather than NLP alone. (LLM, conf=0.84)
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- Authors
- Tierney, P.
- Affiliations
- Brock University (CA)
- Corresponding author
- Tierney, Patrick
- Venue
- The International Review of Research in Open and Distributed Learning
- Publisher
- Athabasca University Press
- Published
- 2012-11-08
- Volume
- 13
- Issue
- 5
- Pages
- 173
- ISSN
- 1492-3831 (ISSN-L)
- OpenAlex
- W1485627804
- Cited by
- 38
- References
- 16
- Citations by year
- 2025: 3 · 2024: 1 · 2023: 5 · 2022: 5 · 2021: 4 · 2020: 3 · 2019: 5 · 2018: 1 · 2016: 4 · 2015: 1 · 2014: 2 · 2013: 4
- FWCI
- 14.93
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Licence
- cc-by
- Locations
- The International Review of Research in Open and Distributed Learning (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Érudit (Université de Montréal) (submittedVersion, cc-by)
- Topics
- Social Sciences › General Social Sciences › Computational and Text Analysis Methods
- Social Sciences › Education › Qualitative Research Methods and Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Sentiment Analysis and Opinion Mining
- Keywords
- qualitative data analysis, natural language processing, graph theory, coding, artificial intelligence, text analysis, research methodology
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