Large Language Model-Augmented Contrastive Learning for Fine-Grained Text Emotion Understanding
Keywords:
fine-grained emotion; text emotion classification; large language model augmentation; contrastive learning; fairness; system governance; sustainable deploymentAbstract
Fine-grained emotion understanding in text remains difficult because emotional expressions are contextual, overlapping, and culturally mediated. This article examines a system-level framework that integrates large language model augmentation with supervised contrastive representation learning for fine-grained text emotion classification. Rather than focusing on a single model architecture, the discussion addresses structural trade-offs across data generation, representation learning, deployment infrastructure, evaluation, and governance. Large language models can provide paraphrases, soft labels, and contextual expansions that enrich contrastive training, while supervised contrastive objectives encourage an embedding space in which utterances with similar fine-grained emotion states are pulled together and dissimilar states are separated. The analysis highlights how these mechanisms interact with fairness, robustness, and sustainability. We argue that system design choices regarding augmentation quality, label granularity, encoder scale, distillation strategy, and feedback loops shape performance more than isolated algorithmic components. Drawing on socio-technical and infrastructural perspectives, the paper presents cross-domain illustrations, identifies failure modes, and discusses policy implications for responsible deployment. The resulting framework is intended as a reference for researchers and practitioners building emotion-aware language systems in sensitive contexts such as mental health, education, and customer care.
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