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

arXiv: https://arxiv.org/abs/2201.06868 · HTML: https://mlsanity.com/paper/2201.06868/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 41 references that are in the map.
- First posted on arXiv 18 Jan 2022.

## Builds on (most cited first, 6 of 6)

- [Training Deep Networks for Facial Expression Recognition with Crowd-Sourced Label Distribution](https://mlsanity.com/paper/1608.01041/) (2016)
- [Frame attention networks for facial expression recognition in videos](https://mlsanity.com/paper/1907.00193/) (2019)
- [Two-Stream Aural-Visual Affect Analysis in the Wild](https://mlsanity.com/paper/2002.03399/) (2020)
- [Facial expression and attributes recognition based on multi-task learning of lightweight neural networks](https://mlsanity.com/paper/2103.17107/) (2021)
- [Speech Emotion Recognition with Dual-Sequence LSTM Architecture](https://mlsanity.com/paper/1910.08874/) (2019)
- [Affect Expression Behaviour Analysis in the Wild using Spatio-Channel Attention and Complementary Context Information](https://mlsanity.com/paper/2009.14440/) (2020)

## Built on by (most cited first, 2 of 2)

- [Large Language Models for Oral History Understanding with Text Classification and Sentiment Analysis](https://mlsanity.com/paper/2508.06729/) (2025)
- [Linguistic features for sentence difficulty prediction in ABSA](https://mlsanity.com/paper/2402.03163/) (2024)

## Abstract (arXiv)

For research in audiovisual interview archives often it is not only of interest what is said but also how. Sentiment analysis and emotion recognition can help capture, categorize and make these different facets searchable. In particular, for oral history archives, such indexing technologies can be of great interest. These technologies can help understand the role of emotions in historical remembering. However, humans often perceive sentiments and emotions ambiguously and subjectively. Moreover, oral history interviews have multi-layered levels of complex, sometimes contradictory, sometimes very subtle facets of emotions. Therefore, the question arises of the chance machines and humans have capturing and assigning these into predefined categories. This paper investigates the ambiguity in human perception of emotions and sentiment in German oral history interviews and the impact on machine learning systems. Our experiments reveal substantial differences in human perception for different emotions. Furthermore, we report from ongoing machine learning experiments with different modalities. We show that the human perceptual ambiguity and other challenges, such as class imbalance and lack of training data, currently limit the opportunities of these technologies for oral history archives. Nonetheless, our work uncovers promising observations and possibilities for further research.