Exploring Research Trends Through Topic Modeling of Scopus Data

The rapid growth of scientific literature has made it increasingly difficult for researchers to stay informed about the latest developments in their field. One way to make sense of this vast amount of information is through topic modeling, a method that automatically identifies themes in a collection of documents. In this study, we applied topic modeling algorithms to a dataset of scientific publications indexed in the Scopus database. The goal was to identify the main topics discussed in these publications and to understand the research trends and areas of focus within a particular field. We used Latent Dirichlet Allocation (LDA) and Latent Semantic Analysis (LSA) to analyze the Scopus data and found that topic modeling can be an effective tool for exploring research trends in science. The results of this study can help researchers stay informed about the latest developments in their field and can aid in identifying new areas of research.