Article: Detecting Favorite Topics in Computing Scientific Literature via Dynamic Topic Modeling

Full identifier: https://w3id.org/np/RAi0yZT6cY9B9y8HRBHUVTb-6ciRq_oRG1frJRH0krG8k

 Article: Detecting Favorite Topics in Computing Scientific Literature via Dynamic Topic Modeling ResearchPaper Example ScholarlyWork ▼ comment approve/disapprove edit as derived nanopublication create new with same template

https://doi.org/10.1109/access.2023.3269660 doi:10.1109/access.2023.3269660 http://www.w3.org/1999/02/22-rdf-syntax-ns#type is a http://purl.org/spar/fabio/ResearchPaper research paper .

https://doi.org/10.1109/access.2023.3269660 doi:10.1109/access.2023.3269660 http://purl.org/dc/terms/title has the title (this is a literal) "Detecting Favorite Topics in Computing Scientific Literature via Dynamic Topic Modeling" .

https://doi.org/10.1109/access.2023.3269660 doi:10.1109/access.2023.3269660 http://purl.org/dc/terms/abstract has the abstract (this is a literal) "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." .

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https://doi.org/10.1109/access.2023.3269660 doi:10.1109/access.2023.3269660 http://purl.org/pav/authoredBy is authored by https://orcid.org/0000-0001-6071-2921 0000-0001-6071-2921 .

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https://orcid.org/0000-0001-6071-2921 0000-0001-6071-2921 http://xmlns.com/foaf/0.1/name has the name (this is a literal) "Márcio Barbado Júnior" .

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https://doi.org/10.1109/access.2023.3269660 doi:10.1109/access.2023.3269660 http://purl.org/pav/authoredBy is authored by https://orcid.org/0000-0001-9166-1741 0000-0001-9166-1741 .

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https://orcid.org/0000-0001-9166-1741 0000-0001-9166-1741 http://xmlns.com/foaf/0.1/name has the name (this is a literal) "Rosa Virginia Encinas Quille" .

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https://doi.org/10.1109/access.2023.3269660 doi:10.1109/access.2023.3269660 http://purl.org/pav/authoredBy is authored by https://orcid.org/0000-0002-8743-4244 0000-0002-8743-4244 .

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https://doi.org/10.1109/access.2023.3269660 doi:10.1109/access.2023.3269660 http://purl.org/pav/authoredBy is authored by https://orcid.org/0000-0003-2031-6443 0000-0003-2031-6443 .

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https://orcid.org/0000-0003-2031-6443 0000-0003-2031-6443 http://xmlns.com/foaf/0.1/name has the name (this is a literal) "Felipe Valencia De Almeida" .

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Tobias Kuhn, 24 Nov 2023, 11:26:16 UTC

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