# Discovering Mental Health Research Topics with Topic Modeling

arXiv: https://arxiv.org/abs/2308.13569 · HTML: https://mlsanity.com/paper/2308.13569/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 23 references that are in the map.
- First posted on arXiv 25 Aug 2023.

## Builds on (most cited first, 8 of 8)

- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction](https://mlsanity.com/paper/1802.03426/) (2018)
- [BERTopic: Neural topic modeling with a class-based TF-IDF procedure](https://mlsanity.com/paper/2203.05794/) (2022)
- [Top2Vec: Distributed Representations of Topics](https://mlsanity.com/paper/2008.09470/) (2020)
- [Topic Modeling Based Multi-modal Depression Detection](https://mlsanity.com/paper/1803.10384/) (2018)
- [Neural Topic Modeling of Psychotherapy Sessions](https://mlsanity.com/paper/2204.10189/) (2022)
- [Mental health concerns prelude the Great Resignation: Evidence from Social Media](https://mlsanity.com/paper/2208.07926/) (2022)
- [Exploring COVID-19 Related Stressors Using Topic Modeling](https://mlsanity.com/paper/2202.00476/) (2022)

## Built on by (most cited first, 2 of 2)

- [Large Language Models Struggle to Describe the Haystack without Human Help: Human-in-the-loop Evaluation of Topic Models](https://mlsanity.com/paper/2502.14748/) (2025)
- [Applying LLM and Topic Modelling in Psychotherapeutic Contexts](https://mlsanity.com/paper/2412.17449/) (2024)

## Abstract (arXiv)

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.