Cross-Modal Modeling of Emotional and Thematic Trajectories in Holocaust Survivor Oral Histories

Abstract

Large-scale corpora of Holocaust testimonies preserve vast amounts of historical, emotional, and narrative information, but their size and complexity can make accurate, systematic analysis challenging. This paper presents a cross-modal computational analysis of emotional and thematic trajectories in the CORHOH corpus, containing 500 Holocaust survivor testimonies as a language resource for computational analysis. We segment each testimony into ten segments and apply sentiment analysis, emotion recognition, and topic modeling to each of these segments to reveal how theme and emotion evolve over time in Holocaust testimonies. Results reveal a sharp decline from pre-war life in wartime and camp experiences, with sentiment and emotion remaining negative in post-war segments. Emotion analysis reveals decreasing joy and increasing sadness and fear during segments related to deportation and concentration camps, with limited emotional recovery. Topic modeling identifies coherent themes that align closely with sentiment and emotional patterns. We systematically examine correlations between sentiment, emotion, and topic trajectories, which demonstrate many strong associations between topic and emotion. This work demonstrates that combining sentiment analysis, emotion recognition, and topic modeling can reveal systematic patterns in large oral history corpora, and shows the value of computational approaches for studying historical narratives like the Holocaust.

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