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Datasets:
LT3
/
UniC

Tasks:
Text Classification
Audio Classification
Image Classification
Languages:
English
Size:
n<1K
Tags:
youtube,
review,
sentiment analysis,
emotion recognition,
unimodality and multimodality
License:
Dataset card Files
xet

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20251208

For academic purposes only.

The sentiment annotations are ready to share. Other annotations will follow soon.

These are the features used in the paper LDW: Label Divergence Weighting for Multimodal Sentiment Analysis.

Contact quanqi.du@ugent.be for the access.

UniC: a Dataset for Emotion Analysis of Videos with Multimodal and Unimodal Labels

UniC is comprised of 965 emotion-rich video clips selected from YouTube, annotated in text, audio, (silent) video and multimodal setups with both categorical and dimensional labels.

Categorical label: disgust, disappointment, neutral, confusion, surprise, contentment, and joy.

Dimensional label: Valence and arousal.

If you use this dataset, please cite our papers:

-- Quanqi Du, Sofie Labat, Thomas Demeester and Veronique Hoste. UniC: a dataset for emotion analysis of videos with multimodal and unimodal labels. Language Resources & Evaluation 59, 2857–2892 (2025). https://doi.org/10.1007/s10579-025-09837-0

-- Quanqi Du, Loic De Langhe, Els Lefever, and Veronique Hoste. 2025. LDW: Label Divergence Weighting for Multimodal Sentiment Analysis. In Proceedings of the 33rd ACM International Conference on Multimedia (MM '25). Association for Computing Machinery, New York, NY, USA, 12342–12351. https://doi.org/10.1145/3746027.3758160

Contact: quanqi.du@ugent.be

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