The field of mental health research is witnessing significant advancements with the integration of artificial intelligence (AI) and natural language processing (NLP) techniques. Recent studies have focused on developing AI-driven support tools, analyzing online peer interactions, and recognizing psychodynamic conflicts from semi-structured interviews. These innovations aim to improve mental health interventions, patient treatment, and disaster response. Notably, the use of large language models (LLMs) has shown promise in detecting mental health disorders, such as PTSD, and analyzing financial texts. Furthermore, the development of novel datasets, like the NRC VAD Lexicon v2, has enabled more accurate sentiment analysis and emotion recognition. Overall, the field is moving towards more sophisticated and personalized AI-driven solutions for mental health support and analysis. Noteworthy papers include: RedditESS, which introduces a novel dataset for understanding effective social support, and AutoPsyC, which proposes a method for recognizing psychodynamic conflicts from semi-structured interviews. Detecting PTSD in Clinical Interviews presents a comparative analysis of NLP methods and LLMs for detecting PTSD, and A Survey of Large Language Models in Mental Health Disorder Detection on Social Media provides an overview of LLM applications in social media data analysis for mental health research.
Advancements in AI-Driven Mental Health Support and Analysis
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RedditESS: A Mental Health Social Support Interaction Dataset -- Understanding Effective Social Support to Refine AI-Driven Support Tools
AutoPsyC: Automatic Recognition of Psychodynamic Conflicts from Semi-structured Interviews with Large Language Models
Socially Constructed Treatment Plans: Analyzing Online Peer Interactions to Understand How Patients Navigate Complex Medical Conditions
NRC VAD Lexicon v2: Norms for Valence, Arousal, and Dominance for over 55k English Terms
A Systematic Evaluation of LLM Strategies for Mental Health Text Analysis: Fine-tuning vs. Prompt Engineering vs. RAG
Multi-Stakeholder Disaster Insights from Social Media Using Large Language Models
Digitally Supported Analysis of Spontaneous Speech (DigiSpon): Benchmarking NLP-Supported Language Sample Analysis of Swiss Children's Speech
An Adaptive Control Approach to Treatment Selection for Substance Use Disorders
Detecting PTSD in Clinical Interviews: A Comparative Analysis of NLP Methods and Large Language Models
A Multi-Level Sentiment Analysis Framework for Financial Texts
A Survey of Large Language Models in Mental Health Disorder Detection on Social Media