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license: cc-by-nc-sa-4.0 |
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language: |
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- en |
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tags: |
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- youtube, |
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- review, |
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- sentiment analysis, |
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- emotion recognition, |
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- unimodality and multimodality |
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task_categories: |
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- text-classification |
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- audio-classification |
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- image-classification |
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size_categories: |
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- n<1K |
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20251212 |
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This work is licensed under <a href="https://creativecommons.org/licenses/by-nc-sa/4.0/">Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International</a>. |
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20251208 |
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For academic purposes only. |
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The sentiment annotations are ready to share. Other annotations will follow soon. |
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These are the features used in the paper LDW: Label Divergence Weighting for Multimodal Sentiment Analysis. |
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Contact quanqi.du@ugent.be for the access. |
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UniC: a Dataset for Emotion Analysis of Videos with Multimodal and Unimodal Labels |
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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. |
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Categorical label: disgust, disappointment, neutral, confusion, surprise, contentment, and joy. |
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Dimensional label: Valence and arousal. |
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If you use this dataset, please cite our papers: |
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-- 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 |
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-- 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 |
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Contact: quanqi.du@ugent.be |
