Linguistic expressions of emotions such as depression, anxiety, and trauma-related states are pervasive in clinical notes, counseling dialogues, and online mental health communities, and accurate recognition of these emotions is essential for clinical triage, risk assessment, and timely intervention. Although large language models (LLMs) have demonstrated strong generalization ability in emotion analysis tasks, their diagnostic reliability in high-stakes, context-intensive medical settings re...
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---
name: towards-efficient-and-robust-linguistic-emotion
title: "Towards Efficient and Robust Linguistic Emotion Diagnosis for Mental Health"
version: 0.0.2
engine: skillxiv-v0.0.2-claude-opus-4.6
license: MIT
url: "https://arxiv.org/abs/2601.13481"
keywords: [Agents, Benchmarking]
description: "Linguistic expressions of emotions such as depression, anxiety, and trauma-related states are pervasive in clinical notes, counseling dialogues, and online mental health communities, and accurate recognition of these emotions is essential for clinical triage, risk assessment, and timely intervention. Although large language models (LLMs) have demonstrated strong generalization ability in emotion analysis tasks, their diagnostic reliability in high-stakes, context-intensive medical settings remai..."
---
## Overview
This skill covers research on towards efficient and robust linguistic emotion diagnosis for mental health. It addresses important challenges in agent development and evaluation.
## Key Insights
The paper provides:
- Novel approaches or frameworks for agent systems
- Empirical evaluation results and benchmarks
- Generalizable principles for practitioners
## When to Use
Use this skill when working on:
- Agent-based systems and applications
- Autonomous reasoning and planning
- Agent performance evaluation and improvement
## When NOT to Use
- For non-agent-related tasks
- When seeking implementation code (consult the paper)
## Resources
- ArXiv Abstract: https://arxiv.org/abs/2601.13481
- Full PDF: https://arxiv.org/pdf/2601.13481
- HTML: https://arxiv.org/html/2601.13481
Refer to the original paper for complete technical details, methodology, and experimental protocols.
