# Analyzing the Influence of Dataset Composition for Emotion Recognition

arXiv: https://arxiv.org/abs/2103.03700 · HTML: https://mlsanity.com/paper/2103.03700/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 7 references that are in the map.
- First posted on arXiv 5 Mar 2021.

## Builds on (most cited first, 2 of 2)

- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [The OMG-Emotion Behavior Dataset](https://mlsanity.com/paper/1803.05434/) (2018)

## Built on by (most cited first, 1 of 1)

- [Bias and Fairness on Multimodal Emotion Detection Algorithms](https://mlsanity.com/paper/2205.08383/) (2022)

## Abstract (arXiv)

Recognizing emotions from text in multimodal architectures has yielded promising results, surpassing video and audio modalities under certain circumstances. However, the method by which multimodal data is collected can be significant for recognizing emotional features in language. In this paper, we address the influence data collection methodology has on two multimodal emotion recognition datasets, the IEMOCAP dataset and the OMG-Emotion Behavior dataset, by analyzing textual dataset compositions and emotion recognition accuracy. Experiments with the full IEMOCAP dataset indicate that the composition negatively influences generalization performance when compared to the OMG-Emotion Behavior dataset. We conclude by discussing the impact this may have on HRI experiments.