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        <title>From Consensus to Split Decisions: ABC-Stratified Sentiment in Holocaust Oral Histories</title>
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        <namePart type="given">Daban</namePart>
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            <title>Proceedings of The Second Workshop on Holocaust Testimonies as Language Resources (HTRes)</title>
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            <namePart type="given">Isuri</namePart>
            <namePart type="family">Anuradha</namePart>
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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 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 \ensuremathąppa, Fleiss’ \ensuremathąppa, 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.</abstract>
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%0 Conference Proceedings
%T From Consensus to Split Decisions: ABC-Stratified Sentiment in Holocaust Oral Histories
%A Jaff, Daban Q.
%Y Anuradha, Isuri
%Y Wynne, Martin
%S Proceedings of The Second Workshop on Holocaust Testimonies as Language Resources (HTRes)
%D 2026
%8 May
%I ELRA Language Resources Association (ELRA)
%C Palma, Mallorca (Spain)
%F jaff-2026-consensus
%X 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 \ensuremathąppa, Fleiss’ \ensuremathąppa, 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.
%R 10.63317/2t969u9fy4gf
%U https://aclanthology.org/2026.htres-2.4/
%U https://doi.org/10.63317/2t969u9fy4gf
%P 29-36

Markdown (Informal)

[From Consensus to Split Decisions: ABC-Stratified Sentiment in Holocaust Oral Histories](https://aclanthology.org/2026.htres-2.4/) (Jaff, htres 2026)

• From Consensus to Split Decisions: ABC-Stratified Sentiment in Holocaust Oral Histories (Jaff, htres 2026)

ACL

• Daban Q. Jaff. 2026. From Consensus to Split Decisions: ABC-Stratified Sentiment in Holocaust Oral Histories. In Proceedings of The Second Workshop on Holocaust Testimonies as Language Resources (HTRes), pages 29–36, Palma, Mallorca (Spain). ELRA Language Resources Association (ELRA).