Discovering Mental Health Research Topics with Topic Modeling
Xin Gao Cem Sazara
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 trained a customized
Sentence-BERT based embedding model leveraging the BERTopic framework. 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 the model, we compared it against two other
state-of-the-art methods: Top2Vec model and LDA-BERT model. The model
demonstrated superior performance in metrics that measure topic diversity and
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.