Domain-Adaptive Large Language Models for Cross-Domain Sentiment Analysis: A Contrastive Learning Perspective
Keywords:
large language models, domain adaptation, sentiment analysis, contrastive learning, system infrastructure, governance, fairness, deploymentAbstract
The rapid proliferation of user-generated content across diverse digital platforms has created an urgent need for sentiment analysis systems that can maintain accuracy when transferred across domains, such as from product reviews to clinical narratives or from financial news to social media discussions. Large language models (LLMs) have recently demonstrated remarkable few-shot and zero-shot capabilities, yet their out-of-the-box performance degrades significantly when the target domain exhibits substantial distributional shifts in vocabulary, syntactic structures, and affective expression patterns. This paper presents a comprehensive systems-level examination of domain-adaptive LLMs for cross-domain sentiment analysis through the lens of contrastive learning. Rather than focusing on a single algorithmic novelty, we investigate the full socio-technical infrastructure required to design, deploy, and govern such systems responsibly. We discuss architectural design choices, including the integration of contrastive objectives with adapter-based parameter-efficient fine-tuning, and analyze the structural trade-offs between representation alignment, computational overhead, and model generalization. Special attention is given to data governance frameworks that span multiple heterogeneous domains, addressing provenance, consent, and evolving privacy regulations. The deployment pipeline is examined from the perspective of model serving, continuous monitoring for domain drift, and the sustainability implications of large-scale training. Robustness and fairness are treated as first-order concerns, with deep analyses of how domain-specific biases can propagate through contrastively aligned representations and what auditing mechanisms are necessary to detect and mitigate such harms. The paper further situates these technical considerations within broader policy contexts, including cross-jurisdictional data sharing, algorithmic accountability mandates, and the potential for polarizing effects when sentiment tools are applied to political or health-related discourse. Through a series of case illustrations spanning e-commerce, healthcare, and financial domains, we map the interplay between technical design and institutional governance. The discussion concludes with forward-looking perspectives on the evolution of domain-adaptive LLMs, emphasizing the need for collaborative frameworks that bring together system architects, domain experts, regulators, and affected communities.
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