Topic Modeling from Bibliometric Research of the 2019–2023 Data through Scopus
Keywords:
Topic modeling, Bibliometric, Data analysis, Mapping ScienceAbstract
This article aims to analyze the titles and indexed keywords of research articles in bibliometric research using topic modeling techniques. The analysis was based on articles collected from the Scopus database between 2019 and 2023, focusing on identifying the most frequently occurring words in the titles and indexed keywords of the articles to reveal the relationships or occurring words among the topics studied in this field in R Studio and Python. The study identified 3,931 relevant articles and topic models based on the most frequently occurring words within the analyzed titles and indexed keywords. These topics primarily encompassed health sciences and medicine, with additional management, social sciences, education, and information technology coverage. The study also revealed that the most commonly indexed keywords did not differ significantly from the frequently occurring words in the analyzed titles, further emphasizing environmental studies. This study was valuable for researchers, academics, and others interested in analyzing research articles using topic modeling or other related techniques.
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