Cross-Domain Transfer Learning for Robust Sentiment Classification in Low-Resource Settings
Keywords:
cross-domain transfer learning; sentiment classification; low-resource natural language processing; domain adaptation; fairness; AI governanceAbstract
The accurate classification of sentiment in text remains an open systems challenge when target domains exhibit scarce labeled data, nonstandard dialects, domain-specific vocabulary, and evolving linguistic conventions. This paper examines cross-domain transfer learning as a structured response to low-resource sentiment classification. It argues that robust performance cannot be achieved through model architecture alone; rather, effective systems require coordinated decisions across representation learning, domain alignment, parameter sharing, data governance, and evaluation infrastructure. We discuss the evolution from static lexical models to contextual pretraining and adversarial domain adaptation, highlighting the structural trade-offs between adaptation capacity, computational cost, and generalization. The paper analyzes architectural alternatives, including parameter-efficient fine-tuning and hybrid attention contrastive frameworks, and situates them within broader socio-technical concerns of fairness, auditability, and sustainable deployment. Through conceptual case illustrations spanning product reviews, clinical narratives, and social media, we show that cross-domain sentiment systems must balance statistical adaptation with operational accountability. The discussion extends to policy implications such as model documentation, data provenance, and participatory evaluation, offering a systems-level research agenda for equitable and durable sentiment classification in low-resource environments. The paper contributes an interdisciplinary synthesis rather than a single algorithmic proposal, emphasizing that robustness in low-resource sentiment classification is an emergent property of architecture, governance, and institutional context.
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