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MentalBERT: Publicly Available Pretrained Language Models for Mental Healthcare
Open accessAbstract
Mental health is a critical issue in modern society, and mental disorders could sometimes turn to suicidal ideation without adequate treatment.Early detection of mental disorders and suicidal ideation from social content provides a potential way for effective social intervention.Recent advances in pretrained contextualized language representations have promoted the development of several domain-specific pretrained models and facilitated several downstream applications.However, there are no existing pretrained language models for mental healthcare.This paper trains and releases two pretrained masked language models, i.e., MentalBERT and MentalRoBERTa, to benefit machine learning for the mental healthcare research community.Besides, we evaluate our trained domain-specific models and several variants of pretrained language models on several mental disorder detection benchmarks and demonstrate that language representations pretrained in the target domain improve the performance of mental health detection tasks.
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Aragón, M. E., López-Monroy, A. P., Montes-y-Gómez, M., & Losada, D. E. (2025) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2025.103217 · Cited by 9
Values, Ethics, Morals? On the Use of Moral Concepts in NLP Research
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Towards Intention Understanding in Suicidal Risk Assessment with Natural Language Processing
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Self-citationLLM-based classifiers for discovering mental disorders
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Iftikhar, Z., Ransom, S., Xiao, A. W., Nugent, N. R., & Huang, J. J. S. (2026) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3817112 · Cited by 41
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Yadav, S., Caragea, C., Zhao, C., Kumari, N. P., Solberg, M. A., & Sharma, T. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-long.495 · Cited by 8
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Farruque, N., Zai͏̈ane, O. R., Goebel, R. G., & Sivapalan, S. (2022) · Workshop on Language Technology for Equality, Diversity, Inclusion (LT-EDI) · doi:10.18653/v1/2022.ltedi-1.21 · Cited by 6
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Metadata
- Authors
- Ji, S., Zhang, T., Ansari, L., Fu, J., Tiwari, P., & Cambria, E.
- Affiliations
- Aalto University (FI)
- University of Manchester (GB)
- Mila - Quebec Artificial Intelligence Institute (CA)
- Nanyang Technological University (SG)
- Venue
- Proceedings of the Language Resources and Evaluation Conference
- Published
- 2022-06
- Pages
- 7184-7190
- ISSN
- 2522-2686
- OpenAlex
- W3209409148
- Cited by
- 233
- References
- 38
- Citations by year
- 2026: 68 · 2025: 30 · 2024: 62 · 2023: 55 · 2022: 18
- FWCI
- 79.56
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by-nc
- Locations
- Language Resources and Evaluation Conference (LREC) (publishedVersion, cc-by-nc)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University) (cc-by)
- Funders
- China Scholarship Council
- Topics
- Social Sciences › Psychology › Social Psychology › Mental Health via Writing
- Physical Sciences › Computer Science › Artificial Intelligence › Sentiment Analysis and Opinion Mining
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Keywords
- pre-trained language models, mental disorder detection, suicidal ideation detection, masked language models, social media text analysis, mental health services
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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