TY  - JOUR
AU  - Tariq, Muhammad Usman
AU  - Ahmed, Sara
AU  - Mahmood, Khalid
PY  - 2026
DA  - 2026/07/15
TI  - Challenges and Applications of Large Language Models in Emotion Analysis for Mental Health: A Mini Review
JO  - Journal of Artificial Intelligence in Bioinformatics
T2  - Journal of Artificial Intelligence in Bioinformatics
JF  - Journal of Artificial Intelligence in Bioinformatics
VL  - 2
IS  - 2
SP  - 31
EP  - 43
DO  - 10.62762/JAIB.2026.988603
UR  - https://www.icck.org/article/abs/JAIB.2026.988603
KW  - large language models
KW  - emotion analysis
KW  - mental health
KW  - natural language processing
KW  - depression detection
KW  - sentiment analysis
KW  - affective computing
AB  - Emotion analysis in mental health has evolved from lexicon-based systems to large language models (LLMs) capable of contextual affect inference, severity estimation, and empathic dialogue generation, reflecting advances in NLP and the recognition that language is a rich proxy for psychological state. This mini review synthesizes LLM applications in mental health emotion analysis, characterizing methodological trends, identifying strengths and limitations, and highlighting critical gaps in benchmarking, clinical validation, and governance. A structured PRISMA-informed search across six databases (2017--2025) using three Boolean keyword clusters yielded 44 studies after two-stage independent screening (Cohen's $\kappa = 0.78$). Data sources included social media, clinical transcripts, EHRs, dialogue data, and multimodal datasets, covering emotion classification, severity prediction, conversational inference, empathy evaluation, and safety detection. LLMs demonstrate strong performance in fine-grained emotion labeling, severity estimation, empathic response generation, and multi-task inference, with domain-adapted encoders (MentalBERT, ClinicalBERT, BioBERT) and clinical LLMs (Med-PaLM, Mental-LLM) further extending coverage. Persistent issues remain, including hallucination risks, demographic bias, lack of prospective validation, and absence of standardized mental health benchmarks. While LLMs offer transformative potential, current evidence supports their use to assist-not replace-clinical judgment; safe deployment requires robust governance, clinically validated benchmarks, and hybrid architectures balancing performance with safety and interpretability.
SN  - 3068-7535
PB  - Institute of Central Computation and Knowledge
LA  - English
ER  - 

@article{Tariq2026Challenges,
  author = {Muhammad Usman Tariq and Sara Ahmed and Khalid Mahmood},
  title = {Challenges and Applications of Large Language Models in Emotion Analysis for Mental Health: A Mini Review},
  journal = {Journal of Artificial Intelligence in Bioinformatics},
  year = {2026},
  volume = {2},
  number = {2},
  pages = {31-43},
  doi = {10.62762/JAIB.2026.988603},
  url = {https://www.icck.org/article/abs/JAIB.2026.988603},
  abstract = {Emotion analysis in mental health has evolved from lexicon-based systems to large language models (LLMs) capable of contextual affect inference, severity estimation, and empathic dialogue generation, reflecting advances in NLP and the recognition that language is a rich proxy for psychological state. This mini review synthesizes LLM applications in mental health emotion analysis, characterizing methodological trends, identifying strengths and limitations, and highlighting critical gaps in benchmarking, clinical validation, and governance. A structured PRISMA-informed search across six databases (2017--2025) using three Boolean keyword clusters yielded 44 studies after two-stage independent screening (Cohen's \$\kappa = 0.78\$). Data sources included social media, clinical transcripts, EHRs, dialogue data, and multimodal datasets, covering emotion classification, severity prediction, conversational inference, empathy evaluation, and safety detection. LLMs demonstrate strong performance in fine-grained emotion labeling, severity estimation, empathic response generation, and multi-task inference, with domain-adapted encoders (MentalBERT, ClinicalBERT, BioBERT) and clinical LLMs (Med-PaLM, Mental-LLM) further extending coverage. Persistent issues remain, including hallucination risks, demographic bias, lack of prospective validation, and absence of standardized mental health benchmarks. While LLMs offer transformative potential, current evidence supports their use to assist-not replace-clinical judgment; safe deployment requires robust governance, clinically validated benchmarks, and hybrid architectures balancing performance with safety and interpretability.},
  keywords = {large language models, emotion analysis, mental health, natural language processing, depression detection, sentiment analysis, affective computing},
  issn = {3068-7535},
  publisher = {Institute of Central Computation and Knowledge}
}