--- license: cc-by-nc-sa-4.0 language: - en tags: - youtube, - review, - sentiment analysis, - emotion recognition, - unimodality and multimodality task_categories: - text-classification - audio-classification - image-classification size_categories: - n<1K --- --- --- 20251212 This work is licensed under Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International. --- 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