Contextual Emotion Detection in Mental Health Support Conversations Using Large Language Models
Keywords:
contextual emotion detection; large language models; mental health; natural language processing; system architecture; fairness; governance; clinical deploymentAbstract
Mental health support increasingly takes place through text-based platforms, including crisis services, telepsychology portals, peer support forums, and mobile counselling applications. In these settings, emotional states must be inferred from written language, often across multiple conversational turns and under conditions of significant ambiguity. Contextual emotion detection therefore requires systems that can interpret not only discrete words but also dialogue history, user goals, social context, and clinical risk. Large language models offer substantial potential for this task because they encode rich linguistic context and can adapt to complex mental health expressions. However, their deployment in mental health infrastructures raises structural, ethical, and operational challenges that extend well beyond model accuracy. This paper presents a system-level analysis of contextual emotion detection in mental health support conversations using large language models. It examines architectural configurations, representation choices, model adaptation, deployment trade-offs, robustness, fairness, governance, sustainability, and clinical integration. The discussion emphasizes that effective emotion detection in mental health is not a standalone classification problem but a socio-technical system design problem. It requires careful balancing of inference quality, latency, privacy, interpretability, and accountability. The paper argues for modular architectures with human oversight, strong documentation practices, and policy alignment. It further considers cross-domain comparisons, energy costs, and the implications of using general-purpose foundation models in sensitive clinical contexts. The analysis is intended to inform researchers, system designers, and policy stakeholders who seek to build safer and more sustainable mental health text analysis infrastructures.
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