Statistical Measures for Explainable Aspect-Based Sentiment Analysis: A Case Study on Environmental Discourse in Reddit
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arXiv
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| Autori principali: | , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866910029281492992 |
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| author | Stracqualursi, Luisa Agati, Patrizia |
| author_facet | Stracqualursi, Luisa Agati, Patrizia |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_19216 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Statistical Measures for Explainable Aspect-Based Sentiment Analysis: A Case Study on Environmental Discourse in Reddit Stracqualursi, Luisa Agati, Patrizia Methodology 62-08, 62F40, 62P25 I.2.7; G.3 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. |
| title | Statistical Measures for Explainable Aspect-Based Sentiment Analysis: A Case Study on Environmental Discourse in Reddit |
| topic | Methodology 62-08, 62F40, 62P25 I.2.7; G.3 |
| url | https://arxiv.org/abs/2602.19216 |