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

2021-07-16Artigo científico Acesso aberto
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datacite.subject.fosCiências Naturais::Matemáticas
datacite.subject.fosEngenharia e Tecnologia
datacite.subject.fosCiências Naturais::Ciências da Computação e da Informação
datacite.subject.sdg12:Produção e Consumo Sustentáveis
datacite.subject.sdg13:Ação Climática
datacite.subject.sdg14:Proteger a Vida Marinha
dc.contributor.authorDe la Hoz-M, Javier
dc.contributor.authorFernández-Gómez, Mª José
dc.contributor.authorMendes, Susana
dc.date.accessioned2026-02-20T18:33:25Z
dc.date.available2026-02-20T18:33:25Z
dc.date.issued2021-07-16
dc.description.abstractIn 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.eng
dc.description.sponsorshipThis work was partially funded by FCT (Fundação para a Ciência e a Tecnologia) for the financial support of this work through the project UID/MAR/04292/2020, attributed to MARE-Marine and Environmental Sciences Centre, Portugal, and by the Integrated Programme of SR&TD “SmartBioR” (reference Cen-tro-01-0145-FEDER-000018), cofounded by the Centro 2020 program, Portugal2020, European Union, through the European Regional Development Fund.
dc.identifier.citationDe 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.
dc.identifier.doi10.3390/math9141671
dc.identifier.eissn2227-7390
dc.identifier.urihttp://hdl.handle.net/10400.8/15694
dc.language.isoeng
dc.peerreviewedyes
dc.publisherMDPI
dc.relation.hasversionhttps://www.mdpi.com/2227-7390/9/14/1671#
dc.relation.ispartofMathematics
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjecttext mining
dc.subjecttopic modeling
dc.subjectlatent dirichlet allocation
dc.subjectautomatic literature review
dc.titleLDAShiny: An R Package for Exploratory Review of Scientific Literature Based on a Bayesian Probabilistic Model and Machine Learning Toolseng
dc.typejournal article
dspace.entity.typePublication
oaire.citation.endPage21
oaire.citation.issue14
oaire.citation.startPage1
oaire.citation.titleMathematics
oaire.citation.volume9
oaire.versionhttp://purl.org/coar/version/c_970fb48d4fbd8a85
person.familyNameMendes
person.givenNameSusana
person.identifier322266
person.identifier.ciencia-id4514-12E2-1FD9
person.identifier.orcid0000-0001-9681-3169
person.identifier.scopus-author-id33568138000
relation.isAuthorOfPublicationdb5be03b-2077-482b-a9cf-8b3e84861276
relation.isAuthorOfPublication.latestForDiscoverydb5be03b-2077-482b-a9cf-8b3e84861276

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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.
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