Analysis of keywords in the field of crisis management using semantic network graphs
DOI:
https://doi.org/10.51381/adrs.v5i1-2.435Keywords:
semantic networks, semantic vectors, keywords, crisis management, unsupervised learningAbstract
This paper attempts to use keywords in order to establish their information value in understanding main topics and trends in crisis management research on the example of the Crisis Management Days conference proceedings. It uses natural language processing methodology, statistical analysis, and design of semantic network graphs to investigate topics and trends in the field of crisis management in Croatia and the region over the span of 13 years. Additional goal of the paper is establishing the validity of semantic distance analysis based solely on author keywords. The results not only reveal certain trends and clusters of conference topics, but show that the method holds potential for further research.
References
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Hagberg, A. A., Schult, D. A., & Swart, P. J. (2008). Exploring Network Structure, Dynamics, and Function using NetworkX. In G. Varoquaux, T. Vaught, & J. Millman (Eds.), Proceedings of the 7th Python in Science Conference (SciPy2008) (pp. 11–15). https://conference.scipy.org/proceedings/SciPy2008/paper_2/
Hunter, J. D. (2007). Matplotlib: A 2D Graphics Environment. Computing in Science & Engineering, 9(3), 90–95. https://doi.org/10.1109/MCSE.2007.55
Lardera, M., & Hjorland, B. (2021). Keyword. KNOWLEDGE ORGANIZATION, 48(6), 430–456. https://doi.org/10.5771/0943-7444-2021-6-430
Manning, C. D., & Schütze, H. (1999). Foundations of statistical natural language processing. The MIT Press.
Pesta, B., Fuerst, J., & Kirkegaard, E. (2018). Bibliometric Keyword Analysis across Seventeen Years (2000–2016) of Intelligence Articles. Journal of Intelligence, 6(4), 46. https://doi.org/10.3390/jintelligence6040046
Řehůřek, R. (2022). Gensim. https://radimrehurek.com/gensim/
Řehůřek, R., & Sojka, P. (2010). Software Framework for Topic Modelling with Large Corpora. Proceedings of the LREC 2010 Workshop on New Challenges for NLP Frameworks, 45–50.
Song, Y., Liu, S., Liu, X., & Wang, H. (2015). Automatic Taxonomy Construction from Keywords via Scalable Bayesian Rose Trees. IEEE Transactions on Knowledge and Data Engineering, 27(7), 1861–1874. https://doi.org/10.1109/TKDE.2015.2397432
Sowa, J. F. (1991). TOWARD THE EXPRESSIVE POWER OF NATURAL LANGUAGE. In Principles of Semantic Networks (pp. 157–189). Elsevier. https://doi.org/10.1016/B978-1-4832-0771-1.50011-4
Su, H. N., & Lee, P. C. (2010). Mapping knowledge structure by keyword co-occurrence: A first look at journal papers in Technology Foresight. Scientometrics, 85(1), 65–79. https://doi.org/10.1007/s11192-010-0259-8
Tang, X., Zheng, Z., Li, J., Meng, F., Zhu, S.-C., Liang, Y., & Zhang, M. (2023). Large Language Models are In-Context Semantic Reasoners rather than Symbolic Reasoners.
Weismayer, C., & Pezenka, I. (2017a). Identifying emerging research fields: a longitudinal latent semantic keyword analysis. Scientometrics, 113(3), 1757–1785. https://doi.org/10.1007/s11192-017-2555-z
Weismayer, C., & Pezenka, I. (2017b). Identifying emerging research fields: a longitudinal latent semantic keyword analysis. Scientometrics, 113(3), 1757–1785. https://doi.org/10.1007/s11192-017-2555-z
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Published
2024-01-23
How to Cite
Bakarić, N. (2024). Analysis of keywords in the field of crisis management using semantic network graphs. Annals of Disaster Risk Sciences, 5(1-2). https://doi.org/10.51381/adrs.v5i1-2.435
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