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

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 Holocaust oral histories, using three pretrained transformer-based polarity classifiers over a corpus comprising 107,304 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’s κ, Fleiss’ κ, and row-normalized confusion matrices to localize systematic disagreement. As an external convergent descriptive signal, we apply a T5-based emotion classifier 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, interpretable 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.