Contrastive Learning for Emotion-Aware Mental Health Text Classification
Keywords:
contrastive learning; mental health text classification; emotion-aware representations; fairness; governance; deploymentAbstract
Mental health conditions are frequently expressed through subtle linguistic and affective cues in digital text, yet automated classification systems often struggle to capture the emotional complexity that distinguishes distress-related language from ordinary negative sentiment. Contrastive learning offers a promising representational strategy for emotion-aware mental health text classification because it encourages models to learn structured embedding spaces in which semantically and emotionally similar expressions are pulled closer together while dissimilar expressions are separated. This paper presents a system-level analysis of contrastive learning for mental health text classification, focusing on architectural choices, data infrastructure, robustness, fairness, governance, and deployment. Rather than treating contrastive learning solely as a training objective, the discussion examines how supervised and self-supervised contrastive mechanisms interact with pretrained language models, annotation pipelines, and clinical deployment constraints. The paper further addresses structural trade-offs in positive pair construction, emotion label granularity, batch composition, and representation reuse across changing populations. Attention is given to privacy, consent, stigmatized language, demographic bias, and regulatory expectations that shape real-world mental health applications. The analysis integrates perspectives from natural language processing, human-computer interaction, fairness research, and digital psychiatry to argue that contrastive learning can improve representation quality only when embedded within a carefully governed socio-technical system. The conclusion identifies open challenges and forward-looking research directions that link technical performance to responsible deployment.
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