https://w3id.org/np/RAi0yZT6cY9B9y8HRBHUVTb-6ciRq_oRG1frJRH0krG8k#Head https://w3id.org/np/RAi0yZT6cY9B9y8HRBHUVTb-6ciRq_oRG1frJRH0krG8k http://www.nanopub.org/nschema#hasAssertion https://w3id.org/np/RAi0yZT6cY9B9y8HRBHUVTb-6ciRq_oRG1frJRH0krG8k#assertion https://w3id.org/np/RAi0yZT6cY9B9y8HRBHUVTb-6ciRq_oRG1frJRH0krG8k http://www.nanopub.org/nschema#hasProvenance https://w3id.org/np/RAi0yZT6cY9B9y8HRBHUVTb-6ciRq_oRG1frJRH0krG8k#provenance https://w3id.org/np/RAi0yZT6cY9B9y8HRBHUVTb-6ciRq_oRG1frJRH0krG8k http://www.nanopub.org/nschema#hasPublicationInfo https://w3id.org/np/RAi0yZT6cY9B9y8HRBHUVTb-6ciRq_oRG1frJRH0krG8k#pubinfo https://w3id.org/np/RAi0yZT6cY9B9y8HRBHUVTb-6ciRq_oRG1frJRH0krG8k http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://www.nanopub.org/nschema#Nanopublication https://w3id.org/np/RAi0yZT6cY9B9y8HRBHUVTb-6ciRq_oRG1frJRH0krG8k#assertion http://id.crossref.org/issn/2169-3536 http://purl.org/dc/terms/title IEEE Access https://doi.org/10.1109/access.2023.3269660 http://purl.org/dc/terms/abstract Topic modeling comprises a set of machine learning algorithms that allow topics to be extracted from a collection of documents. These algorithms have been widely used in many areas, such as identifying dominant topics in scientific research. However, works addressing such problems focus on identifying static topics, providing snapshots that cannot show how those topics evolve. Aiming to close this gap, in this article, we describe an approach for dynamic article set analysis and classification. This is accomplished by querying open data of notable scientific databases via representational state transfers. After that, we enforce data management practices with a dynamic topic modeling approach on the associated metadata available. As a result, we identify research trends for a given field at specific instants and the referred terminology trends evolution throughout the years. It was possible to detect the associated lexical variation over time in published content, ultimately determining the so-called “hot topics” in arbitrary instants and how they correlate. https://doi.org/10.1109/access.2023.3269660 http://purl.org/dc/terms/date 2023 https://doi.org/10.1109/access.2023.3269660 http://purl.org/dc/terms/isPartOf http://id.crossref.org/issn/2169-3536 https://doi.org/10.1109/access.2023.3269660 http://purl.org/dc/terms/title Detecting Favorite Topics in Computing Scientific Literature via Dynamic Topic Modeling https://doi.org/10.1109/access.2023.3269660 http://purl.org/pav/authoredBy https://orcid.org/0000-0001-6071-2921 https://doi.org/10.1109/access.2023.3269660 http://purl.org/pav/authoredBy https://orcid.org/0000-0001-9166-1741 https://doi.org/10.1109/access.2023.3269660 http://purl.org/pav/authoredBy https://orcid.org/0000-0002-8743-4244 https://doi.org/10.1109/access.2023.3269660 http://purl.org/pav/authoredBy https://orcid.org/0000-0003-2031-6443 https://doi.org/10.1109/access.2023.3269660 http://purl.org/pav/authoredBy https://orcid.org/0000-0003-3035-1162 https://doi.org/10.1109/access.2023.3269660 http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://purl.org/spar/fabio/ResearchPaper https://orcid.org/0000-0001-6071-2921 http://schema.org/affiliation https://w3id.org/np/RAi0yZT6cY9B9y8HRBHUVTb-6ciRq_oRG1frJRH0krG8k#USP https://orcid.org/0000-0001-6071-2921 http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://xmlns.com/foaf/0.1/Person https://orcid.org/0000-0001-6071-2921 http://xmlns.com/foaf/0.1/name Márcio Barbado Júnior https://orcid.org/0000-0001-9166-1741 http://schema.org/affiliation https://w3id.org/np/RAi0yZT6cY9B9y8HRBHUVTb-6ciRq_oRG1frJRH0krG8k#USP https://orcid.org/0000-0001-9166-1741 http://schema.org/email encinas@usp.br https://orcid.org/0000-0001-9166-1741 http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://xmlns.com/foaf/0.1/Person https://orcid.org/0000-0001-9166-1741 http://xmlns.com/foaf/0.1/name Rosa Virginia Encinas Quille https://orcid.org/0000-0002-8743-4244 http://schema.org/affiliation https://w3id.org/np/RAi0yZT6cY9B9y8HRBHUVTb-6ciRq_oRG1frJRH0krG8k#USP https://orcid.org/0000-0002-8743-4244 http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://xmlns.com/foaf/0.1/Person https://orcid.org/0000-0002-8743-4244 http://xmlns.com/foaf/0.1/name Pedro Luiz Pizzigatti Corrêa https://orcid.org/0000-0003-2031-6443 http://schema.org/affiliation https://w3id.org/np/RAi0yZT6cY9B9y8HRBHUVTb-6ciRq_oRG1frJRH0krG8k#USP https://orcid.org/0000-0003-2031-6443 http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://xmlns.com/foaf/0.1/Person https://orcid.org/0000-0003-2031-6443 http://xmlns.com/foaf/0.1/name Felipe Valencia De Almeida https://orcid.org/0000-0003-3035-1162 http://schema.org/affiliation https://w3id.org/np/RAi0yZT6cY9B9y8HRBHUVTb-6ciRq_oRG1frJRH0krG8k#USP https://orcid.org/0000-0003-3035-1162 http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://xmlns.com/foaf/0.1/Person 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