Domain-Adaptive Sentiment Analysis for Financial News and Investor Opinions
Keywords:
domain adaptation, financial sentiment analysis, natural language processing, AI governance, robustness, fairness, socio-technical infrastructureAbstract
The reliable interpretation of sentiment in financial news and investor opinions remains a difficult systems challenge because financial language is context-dependent, temporally unstable, and institutionally embedded. This paper examines domain-adaptive sentiment analysis from a systems perspective, treating model architecture, data infrastructure, transfer mechanisms, and deployment governance as interdependent components rather than isolated tasks. We argue that financial sentiment systems must address distributional shift across sources, market conditions, and investor communities, and that adaptation mechanisms such as adversarial alignment and contrastive representation learning can improve robustness while also introducing new governance obligations. The paper analyzes structural trade-offs among supervised in-domain training, cross-domain generalization, computational cost, auditability, and fairness. It further considers deployment concerns including drift monitoring, human oversight, model reporting, dataset documentation, and regulatory alignment. Throughout, the discussion connects algorithmic choices to organizational and policy implications, using financial sentiment analysis as a case for the broader design of adaptive socio-technical systems. The paper concludes that sustainable domain-adaptive sentiment infrastructures require not only better transfer learning but also formalized feedback loops, institutional accountability, and context-aware evaluation.
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