From Consensus to Split Decisions: ABC-Stratified Sentiment in Holocaust Oral Histories

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Abstract:Polarity detection becomes substantially more challenging under domain shift, particularly in heterogeneous, long-form narratives with complex discourse structure, such as Holocaust oral histories. This paper presents a corpus-scale diagnostic study of off-the-shelf sentiment classifiers on long-form Holocaust oral histories, using three pretrained transformer-based polarity classifiers on a corpus of 107,305 utterances and 579,013 sentences. After assembling model outputs, we introduce an agreement-based stability taxonomy (ABC) to stratify inter-model output stability. We report pairwise percent agreement, Cohen kappa, Fleiss kappa, and row-normalized confusion matrices to localize systematic disagreement. As an auxiliary descriptive signal, a T5-based emotion classifier is applied to stratified samples from each agreement stratum to compare emotion distributions across strata. The combination of multi-model label triangulation and the ABC taxonomy provides a cautious, operational framework for characterizing where and how sentiment models diverge in sensitive historical narratives. Inter-model agreement is low to moderate overall and is driven primarily by boundary decisions around neutrality.

Subjects:

Computation and Language (cs.CL)

Cite as: arXiv:2603.28913 [cs.CL]

(or arXiv:2603.28913v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2603.28913

arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Daban Q. Jaff Mr [view email] [v1] Mon, 30 Mar 2026 18:44:22 UTC (319 KB)