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For research in audiovisual interview archives often it is not only ofinterest what is said but also how. Sentiment analysis and emotion recognitioncan help capture, categorize and make these different facets searchable. Inparticular, for oral history archives, such indexing technologies can be ofgreat interest. These technologies can help understand the role of emotions inhistorical remembering. However, humans often perceive sentiments and emotionsambiguously and subjectively. Moreover, oral history interviews havemulti-layered levels of complex, sometimes contradictory, sometimes very subtlefacets of emotions. Therefore, the question arises of the chance machines andhumans have capturing and assigning these into predefined categories. Thispaper investigates the ambiguity in human perception of emotions and sentimentin German oral history interviews and the impact on machine learning systems.Our experiments reveal substantial differences in human perception fordifferent emotions. Furthermore, we report from ongoing machine learningexperiments with different modalities. We show that the human perceptualambiguity and other challenges, such as class imbalance and lack of trainingdata, currently limit the opportunities of these technologies for oral historyarchives. Nonetheless, our work uncovers promising observations andpossibilities for further research.

A Study on the Ambiguity in Human Annotation of German Oral History Interviews for Perceived Emotion Recognition and Sentiment Analysis