  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.
