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Modeling emotion in complex stories: the Stanford Emotional Narratives Dataset

Desmond C. Ong·Zhengxuan Wu·Tan Zhi-Xuan·Marianne Reddan·Isabella Kahhale·Alison Mattek·Jamil Zaki
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Abstract

Human emotions unfold over time, and more affective computing research has to prioritize capturing this crucial component of real-world affect. Modeling dynamic emotional stimuli requires solving the twin challenges of time-series modeling and of collecting high-quality time-series datasets. We begin by assessing the state-of-the-art in time-series emotion recognition, and we review contemporary time-series approaches in affective computing, including discriminative and generative models. We then introduce the first version of the Stanford Emotional Narratives Dataset (SENDv1): a set of rich, multimodal videos of self-paced, unscripted emotional narratives, annotated for emotional valence over time. The complex narratives and naturalistic expressions in this dataset provide a challenging test for contemporary time-series emotion recognition models. We demonstrate several baseline and state-of-the-art modeling approaches on the SEND, including a Long Short-Term Memory model and a multimodal Variational Recurrent Neural Network, which perform comparably to the human-benchmark. We end by discussing the implications for future research in time-series affective computing.

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Desmond C. Ong, Zhengxuan Wu, Tan Zhi-Xuan, Marianne Reddan, Isabella Kahhale, Alison Mattek, Jamil Zaki. 2019-11-22. Modeling emotion in complex stories: the Stanford Emotional Narratives Dataset. https://doi.org/10.1109/taffc.2019.2955949

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