Matches in Nanopublications for { ?s ?p ?o <https://w3id.org/np/RAi0yZT6cY9B9y8HRBHUVTb-6ciRq_oRG1frJRH0krG8k#assertion>. }
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- 0000-0001-9166-1741 type Person assertion.
- 0000-0001-6071-2921 type Person assertion.
- 0000-0002-8743-4244 type Person assertion.
- 0000-0003-2031-6443 type Person assertion.
- 0000-0003-3035-1162 type Person assertion.
- access.2023.3269660 type ResearchPaper assertion.
- USP type Organization assertion.
- access.2023.3269660 authoredBy 0000-0001-9166-1741 assertion.
- access.2023.3269660 authoredBy 0000-0001-6071-2921 assertion.
- access.2023.3269660 authoredBy 0000-0002-8743-4244 assertion.
- access.2023.3269660 authoredBy 0000-0003-2031-6443 assertion.
- access.2023.3269660 authoredBy 0000-0003-3035-1162 assertion.
- access.2023.3269660 isPartOf 2169-3536 assertion.
- access.2023.3269660 title "Detecting Favorite Topics in Computing Scientific Literature via Dynamic Topic Modeling" assertion.
- 2169-3536 title "IEEE Access" assertion.
- 0000-0001-9166-1741 name "Rosa Virginia Encinas Quille" assertion.
- 0000-0001-6071-2921 name "Márcio Barbado Júnior" assertion.
- 0000-0002-8743-4244 name "Pedro Luiz Pizzigatti Corrêa" assertion.
- 0000-0003-2031-6443 name "Felipe Valencia De Almeida" assertion.
- 0000-0003-3035-1162 name "José Meléndez Barros" assertion.
- USP name "University of São Paulo" assertion.
- access.2023.3269660 date "2023" assertion.
- access.2023.3269660 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." assertion.
- 0000-0001-9166-1741 email "encinas@usp.br" assertion.
- 0000-0001-9166-1741 affiliation USP assertion.
- 0000-0001-6071-2921 affiliation USP assertion.
- 0000-0002-8743-4244 affiliation USP assertion.
- 0000-0003-2031-6443 affiliation USP assertion.
- 0000-0003-3035-1162 affiliation USP assertion.