No 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 the identification of dominant topics in scientific research. However, works addressing such problems often focus on identifying static topics, providing snapshots that are unable to show how those topics evolve over time. 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 and subsequently enforcing 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 also how they are correlated to each other.