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In Psychology, health & medicine

The popularity of research topics in clinical psychology has always been changing over time. In this study, we use Latent Dirichlet Allocation (LDA), a well-established statistical modeling approach in machine learning, to extract hot research topics in published review articles in clinical psychology. In Study 1, we use LDA to extract existing topics between 1981 to 2018 from the review articles published on three premium journals in clinical psychology. Results provide stable information about all topics and their proportions. In Study 2, we use a dynamic variant of LDA to identify the development of hot topics from 2007 to 2018. Results show that meta-analysis, psychotherapy, professional development, and depression constantly stay as hot topics all over the 12 years. We also find that behavior intervention has a clear rising trend since 2007. Our results provide a comprehensive summary of the popularity of research topics in clinical psychology in the last couple of years, and the results here can help clinical researchers form a structured view of past research and plan future research directions.

Liu Shuang, Zhang Ru-Yuan, Kishimoto Tomoko


Clinical psychology, LDA Latent Dirichlet Allocation, research trends, topic models