Aspect-Based Sentiment Analysis (ABSA) provides a fine-grained understanding of opinions by linking sentiment to specific aspects in text. While transformer-based models excel at this task, their black-box nature limits their interpretability, posing risks in real-world applications without labeled data. This paper introduces a statistical, model-agnostic framework to assess the behavioral transparency and trustworthiness of ABSA models. Our framework relies on several metrics, such as the entropy of polarity distributions, soft-count-based dominance scores, and sentiment divergence between sources, whose robustness is validated through bootstrap resampling and sensitivity analysis. A case study on environmentally focused Reddit communities illustrates how the proposed indicators provide interpretable diagnostics of model certainty, decisiveness, and cross-source variability. The results show that statistical indicators computed on soft outputs can complement traditional approaches, offering a computationally efficient methodology for validating, monitoring, and interpreting ABSA models in contexts where labeled data are unavailable.

翻译：方面级情感分析（ABSA）通过将情感与文本中的特定方面关联，提供细粒度的观点理解。尽管基于Transformer的模型在此任务中表现优异，但其黑盒特性限制了可解释性，在缺乏标注数据的实际应用中带来风险。本文提出一种与模型无关的统计框架，用于评估ABSA模型的行为透明度和可信度。该框架依赖多项度量指标，如极性分布的熵、基于软计数的支配分数以及源间情感分歧度，其鲁棒性通过自助重采样和敏感性分析进行验证。通过对环保主题Reddit社区的案例研究，展示了所提指标如何为模型确定性、决策力和跨源变异性提供可解释的诊断。结果表明，基于软输出计算的统计指标能够补充传统方法，为标注数据缺失场景下的ABSA模型验证、监控与解释提供计算高效的方法论。