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

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...

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Bibliographic Details
Published inMathematics (Basel) Vol. 9; no. 14; p. 1671
Main Authors De la Hoz-M, Javier, Fernández-Gómez, Mª José, Mendes, Susana
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.07.2021
Subjects
Algorithms
automatic literature review
Body weight
Data mining
Dirichlet problem
Food science
Graphical user interface
Heavy metals
Information processing
Labeling
latent dirichlet allocation
Literature reviews
Machine learning
Preprocessing
Probabilistic models
Researchers
Semantic analysis
Semantics
text mining
Tilapia
topic modeling
Trends
User interface
Variables
Water quality
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Summary: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.
ISSN:2227-7390
2227-7390
DOI:10.3390/math9141671