Discovering Research Topics from Medical Librarianship and Information Using Text Mining

An increasing number of articles published in different scientific fields makes it necessary to analyze the topics of these articles in specialized journals. For this purpose, topics published in the studies on medical librarianship and information in specialized journals were identified and analyzed in the present research. In the present study, an exploratory and descriptive approach was used to analyze medical librarianship and information articles published in specialized journals of this field from 1964 to 2019 by employing text-mining techniques. A latent Dirichlet Allocation (LDA) topic modeling algorithm was used to identify the published topics. Python programming language was also used to run text-mining algorithms. The findings of text mining and topic modeling showed that the following topics were published in medical librarianship and information: Patients' use of information resources (34%), Medical Librarianship and Information Services (18%), Scientometrics and bibliometrics (16.32%), Web-based treatment (15.47%), Information literacy and information skills (13.9%), and Trend and tweet analysis (1.92%). The publishing trend of articles in the medical librarianship and information indicates a change in research in the field.