| Full text | |
| Author(s): |
Quille, Rosa Virginia Encinas
;
Barros, Jose Melendez
;
Barbado Junior, Marcio
;
De Almeida, Felipe Valencia
;
Pizzigatti Correa, Pedro Luiz
Total Authors: 5
|
| Document type: | Journal article |
| Source: | IEEE ACCESS; v. 11, p. 11-pg., 2023-01-01. |
| 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. (AU) | |
| FAPESP's process: | 19/21693-0 - Information system for analysis of large volumes of contaminated areas data |
| Grantee: | Rosa Virginia Encinas Quille |
| Support Opportunities: | Scholarships in Brazil - Doctorate |