Explainable Sentiment Analysis with Attention-Based Evidence Extraction for Legal Text
Keywords:
explainable sentiment analysis; attention-based evidence extraction; legal text; model governance; interpretability; fairness; sociotechnical systemsAbstract
Legal texts contain layered forms of evaluation, approval, risk, obligation, and censure that cannot be reduced to simple positive or negative polarity. Explainable sentiment analysis for legal documents therefore requires evidence extraction mechanisms that connect predictive outputs with identifiable textual spans while respecting institutional and regulatory constraints. This paper presents a system-level examination of attention-based evidence extraction for legal sentiment analysis. It integrates transformer architectures, long-document modeling, post hoc explanation methods, and legal domain adaptation into a broader sociotechnical framework. The central argument is that attention weights should be treated as candidate evidence selectors rather than inherently faithful explanations, and that their utility depends on architectural design, corpus governance, evaluation protocols, and deployment context. The discussion addresses structural trade-offs among predictive accuracy, interpretability, and legal accountability. It also examines the risks of overtrusting salience maps in adversarial or distribution-shifted legal settings. Fairness, robustness, sustainability, auditability, and policy considerations are treated as first-order requirements rather than secondary concerns. Rather than proposing a single technical model, the paper develops an analytical systems perspective showing how attention-based evidence extraction can be embedded within accountable legal artificial intelligence pipelines. The paper concludes that explainable legal sentiment analysis is best understood as a sociotechnical infrastructure whose legitimacy depends on institutional safeguards as much as on algorithmic transparency.
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