doi:10.1109/access.2023.3269660

Full identifier: https://doi.org/10.1109/access.2023.3269660

Assigned to 2 classes:

References

Nanopublication Part Subject Predicate Object Published By Published On
links a nanopublication to its assertion http://www.nanopub.org/nschema#hasAssertion assertion
doi:10.1109/access.2023.3269660
Tobias Kuhn
2023-11-24T11:26:16.582Z
links a nanopublication to its assertion http://www.nanopub.org/nschema#hasAssertion assertion
doi:10.1109/access.2023.3269660
2023
Tobias Kuhn
2023-11-24T11:26:16.582Z
links a nanopublication to its assertion http://www.nanopub.org/nschema#hasAssertion assertion
doi:10.1109/access.2023.3269660
Detecting Favorite Topics in Computing Scientific Literature via Dynamic Topic Modeling
Tobias Kuhn
2023-11-24T11:26:16.582Z
links a nanopublication to its assertion http://www.nanopub.org/nschema#hasAssertion assertion
doi:10.1109/access.2023.3269660
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.
Tobias Kuhn
2023-11-24T11:26:16.582Z
links a nanopublication to its assertion http://www.nanopub.org/nschema#hasAssertion assertion
doi:10.1109/access.2023.3269660
Tobias Kuhn
2023-11-24T11:26:16.582Z
links a nanopublication to its assertion http://www.nanopub.org/nschema#hasAssertion assertion
doi:10.1109/access.2023.3269660
(unknown)
2023-11-22T19:26:28.402Z
links a nanopublication to its assertion http://www.nanopub.org/nschema#hasAssertion assertion
doi:10.1109/access.2023.3269660
2023
(unknown)
2023-11-22T19:26:28.402Z
links a nanopublication to its assertion http://www.nanopub.org/nschema#hasAssertion assertion
doi:10.1109/access.2023.3269660
Detecting Favorite Topics in Computing Scientific Literature via Dynamic Topic Modeling
(unknown)
2023-11-22T19:26:28.402Z
links a nanopublication to its assertion http://www.nanopub.org/nschema#hasAssertion assertion
doi:10.1109/access.2023.3269660
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.
(unknown)
2023-11-22T19:26:28.402Z
links a nanopublication to its assertion http://www.nanopub.org/nschema#hasAssertion assertion
doi:10.1109/access.2023.3269660
(unknown)
2023-11-22T19:26:28.402Z

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