Leveraging AI-Driven Topic Modeling for Business Analytics: A Bibliometric Analysis of Trends in Antidepressant Efficacy

To uncover thematic trends and patterns through studying articles from 2000 to 2023, this study conducts a bibliometric analysis of research on the efficacy of antidepressants, applying Latent Dirichlet Allocation (LDA). Drawing on data from Scopus, the analysis covers 494 records, shedding light on prominent topics, key studies, and emerging areas in the field. The results indicate a significant rise in research activity, especially around 2010 and after 2018, pointing to an increasing focus on the effectiveness of mental health treatments. The thematic analysis revealed five core areas: treatment efficacy, clinical trials, alternative treatment approaches, efficacy compared to side effects, and outcomes centered on patient experience. These findings underscore critical advances and existing gaps in the research, offering direction for future studies and clinical applications. This work demonstrates how integrating bibliometric methods, and LDA can deepen our understanding and support better decision-making in mental healthcare.