Discovering Mental Health Research Topics with Topic Modeling

Mental health significantly influences various aspects of our daily lives, and its importance has been increasingly recognized by the research community and the general public, particularly in the wake of the COVID-19 pandemic. This heightened interest is evident in the growing number of publications dedicated to mental health in the past decade. In this study, our goal is to identify general trends in the field and pinpoint high-impact research topics by analyzing a large dataset of mental health research papers.To accomplish this, we collected abstracts from various databases and leveraged a learned Sentence-BERT based embedding model to analyze the evolution of topics over time. Our dataset comprises 96,676 research papers pertaining to mental health, enabling us to examine the relationships between different topics using their abstracts. To evaluate the effectiveness of our proposed model, we compared it against two other state-of-the-art methods: Top2Vec and LDA-BERT model. Our model demonstrated superior performance in metrics such as TD Inv. RBO (Inverse Rank-Biased Overlap) and TC Cv (Coefficient of Topic Coherence). To enhance our analysis, we also generated word clouds to provide a comprehensive overview of the machine learning models applied in mental health research, shedding light on commonly utilized techniques and emerging trends. Furthermore, we provide a GitHub link to the dataset used in this paper, ensuring its accessibility for further research endeavors.