Statistical measures for explainable aspect-based sentiment analysis: a case study on environmental discourse in Reddit

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.