Text Mining of Journal Article Titles: An LDA-Based Topic Modeling Approach
Keywords:Data Mining, Latent Dirichlet Allocation, Text Mining, Topic Modeling
Among the techniques of text mining, topic modeling is considered one of the emerging tools to extract or detect hidden themes that lie within a huge collection of textual data. Latent Dirichlet Allocation (LDA) is considered a popular method in the field of topic modeling. This paper deals with topic modeling from 9130 articles of Sri Lankan authors having a minimum of 5 citations downloaded from the WoS database using LDA. The LDA tuning (R package) is used in the study to take various measurements for deciding subjects in light of factual elements. The top 10 latent topics were identified, and different unique terms associated with the topics were also discussed. Health is traced as the most occurring latent topic followed by forest and solar cells. Topic-1 (100%) Contains Water-related terms, which is around 60%; Irrigation and soilrelated were 40% (1997). This first topic was prominent across the period barring 1994 and 1996. Topic 3 has gradually decreased and Topic 9 has gradually increased during the last five decades. By comparing our results to traditional scholarship by Sri Lankan authors and the evolution of scientific publication by the island nation, we have shown that topic models can emerge as a scientific alternative to conventional classification systems.
Anupriya, P. (2015). LDA-based topic modeling of journal abstracts. 2015 International Conference on Advanced Computing and Communication Systems. https://doi. org/10.1109/ICACCS.2015.7324058
Arun, R., Suresh, V., Madhavan, C. E. V., and Murthy, M. N. N. (2010). On finding the natural number of topics with latent Dirichlet allocation: Some observations. Advances in Knowledge Discovery and Data Mining. https://doi. org/10.1007/978-3-642-13657-3_43
Asmussen, C. B., and Møller, C. (2019). Smart literature review: A practical topic modelling approach to exploratory literature review. Journal of Big Data, 6(1), Article 93. https://doi. org/10.1186/s40537-019-0255-7
Babu, M. S., Ali, M. A., and Rao, M. A. (2014). A study on information retrieval methods in text mining. International Journal of Engineering Research and Technology, 2(1), 184–190.
Bansal, S. (2016). Beginners guide to topic modeling in Python. Analytics Vidya. Available at: https://www.analyticsvidhya. com/blog/2016/08/beginners-guide-to-topic-modeling-inpython/
Blei, D. M., Ng, A. Y., and Jordan, M. I. (2003). Latent Dirichlet allocation. Journal of Machine Learning Research, 3(1), 993–1022.
Cao, J., Xia, T., Li, J., Zhang, Y, and Tang, S. (2009). A density-based method for adaptive LDA model selection. Neurocomputing, 72(7–9), 1775–1781. https://doi. org/10.1016/j.neucom.2008.06.011
Deveaud, R., Steyvers, É., and Bellot, P. (2014). Accurate and effective latent concept modeling for ad hoc information retrieval. Document Numérique, 17(1), 61–84. https://doi. org/10.3166/dn.17.1.61-84
Griffiths, T. L., and Steyvers, M. (2004). Finding scientific topics. Proceedings of the National Academy of Sciences of the United States of America, 101(Suppl. 1), 5228–5235. https://doi.org/10.1073/pnas.0307752101 PMid:14872004 PMCid:PMC387300
Hannigan, T., Haans, R. F. J., Vakili, K., Tchalian, H., Glaser, V., Wang, M., and Jennings, P. D. (2019). Topic modeling in management research: Rendering new theory from textual data. Academy of Management Annals, 13(2). https://doi. org/10.5465/annals.2017.0099
Jelodar, H., Wang, Y., Yuan, C., Feng, X., Jiang, X., Li, Y., and Zhao, L. (2019). Latent Dirichlet Allocation (LDA) and topic modeling: Models, applications, a survey. Multimedia Tools and Applications, 78(11), 15169-15211. https://doi. org/10.1007/s11042-018-6894-4
Leopold, E., May, M., and Fraunhofer, G. P. (2005). Data mining and text mining for science and technology research. (H. F. Moed, W. G. Steunpunt, & U. Schmoch), Handbook of quantitative science and technology research (pp. 187-214). https://doi.org/10.1007/1-4020-2755-9_9
Mazumder, S., and Barui, T. Discovering topics from the titles of the Indian LIS theses (2021). Library Philosophy and Practice (e-journal). 5924. https://digitalcommons.unl. edu/libphilprac/5924
Muchene, L., and Safari, W. (2021). Two-stage topic modelling of scientific publications: A case study of University of Nairobi, Kenya, PLoS One, 16(1): Article e0243208. https:// doi.org/10.1371/journal.pone.0243208 PMid:33411774 PMCid:PMC7790388
Owa, D. L. M. (2021). Identification of topics from scientific papers through topic modeling. Open Journal of Applied Sciences, 10(04), 541–548. https://doi.org/10.4236/ojapps.2021.104038
Srinivasa-Desikan, B. (2018) Natural language processing and computational linguistics: A practical guide to text analysis with Python, Gensim, SpaCy and Keras. Packt Publishing Ltd., Birmingham.
Storopoli, J. E. (2019). Topic modeling: How and why to use in management research. RevistaIbero-Americana de Estratégia, 18(3), 316–338. https://doi.org/10.5585/ijsm. v18i3.14561
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