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Article

LDAShiny: An R Package for Exploratory Review of Scientific Literature Based on a Bayesian Probabilistic Model and Machine Learning Tools

1
Facultad de Ingeniería, Universidad del Magdalena, Santa Marta 470004, Colombia
2
Department of Statistics, University of Salamanca, 37008 Salamanca, Spain
3
Institute of Biomedical Research of Salamanca, 37008 Salamanca, Spain
4
MARE, School of Tourism and Maritime Technology, Polytechnic of Leiria, 2520-614 Peniche, Portugal
*
Author to whom correspondence should be addressed.
Academic Editor: Maria Laura Manca
Mathematics 2021, 9(14), 1671; https://doi.org/10.3390/math9141671
Received: 17 May 2021 / Revised: 22 June 2021 / Accepted: 24 June 2021 / Published: 16 July 2021
(This article belongs to the Special Issue Multivariate Statistics: Theory and Its Applications)
In this paper we propose an open source application called LDAShiny, which provides a graphical user interface to perform a review of scientific literature using the latent Dirichlet allocation algorithm and machine learning tools in an interactive and easy-to-use way. The procedures implemented are based on familiar approaches to modeling topics such as preprocessing, modeling, and postprocessing. The tool can be used by researchers or analysts who are not familiar with the R environment. We demonstrated the application by reviewing the literature published in the last three decades on the species Oreochromis niloticus. In total we reviewed 6196 abstracts of articles recorded in Scopus. LDAShiny allowed us to create the matrix of terms and documents. In the preprocessing phase it went from 530,143 unique terms to 3268. Thus, with the implemented options the number of unique terms was reduced, as well as the computational needs. The results showed that 14 topics were sufficient to describe the corpus of the example used in the demonstration. We also found that the general research topics on this species were related to growth performance, body weight, heavy metals, genetics and water quality, among others. View Full-Text
Keywords: text mining; topic modeling; latent dirichlet allocation; automatic literature review text mining; topic modeling; latent dirichlet allocation; automatic literature review
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MDPI and ACS Style

De la Hoz-M, J.; Fernández-Gómez, M.J.; Mendes, S. LDAShiny: An R Package for Exploratory Review of Scientific Literature Based on a Bayesian Probabilistic Model and Machine Learning Tools. Mathematics 2021, 9, 1671. https://doi.org/10.3390/math9141671

AMA Style

De la Hoz-M J, Fernández-Gómez MJ, Mendes S. LDAShiny: An R Package for Exploratory Review of Scientific Literature Based on a Bayesian Probabilistic Model and Machine Learning Tools. Mathematics. 2021; 9(14):1671. https://doi.org/10.3390/math9141671

Chicago/Turabian Style

De la Hoz-M, Javier, Mª J. Fernández-Gómez, and Susana Mendes. 2021. "LDAShiny: An R Package for Exploratory Review of Scientific Literature Based on a Bayesian Probabilistic Model and Machine Learning Tools" Mathematics 9, no. 14: 1671. https://doi.org/10.3390/math9141671

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