UniC: a dataset for emotion analysis of videos with multimodal and unimodal labels.
Emotion is a key characteristic that differentiates humans from machines. It is intricate, encompassing a wide variety of emotional states, and is expressed through both verbal and non-verbal communication channels. Different modalities contribute in unique ways to the integrated expression of emoti...
| Publicado en: | Language Resources & Evaluation Vol. 59; no. 3; pp. 2857 - 2893 |
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| Autores principales: | , , , |
| Formato: | Artículo |
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Springer Nature
Sep2025
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| Materias: | |
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=hlh&AN=186909093&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 186909093 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 1574020X 179V jtl: Language Resources & Evaluation issn: 1574020X maglogo: N pubinfo: dt: Sep2025 vid: 59 iid: 3 pid: 237 pub: Springer Nature artinfo: ui: 186909093 10.1007/s10579-025-09837-0 ppf: 2857 ppct: 36 formats: fmt: – @attributes: type: T – @attributes: type: P size: 3.5MB tig: atl: UniC: a dataset for emotion analysis of videos with multimodal and unimodal labels. aug: au: Du, Quanqi Labat, Sofie Demeester, Thomas Hoste, Veronique affil: https://ror.org/00cv9y106 LT3, Ghent University, Groot-Brittanniëlaan 45, 9000, Ghent, Flanders, Belgium https://ror.org/00cv9y106 IDLab, Ghent University-imec, Technologiepark-Zwijnaarde 126, 9052, Ghent, Flanders, Belgium su: Emotional state Emotions Applied sciences Acquisition of data Video excerpts Sentiment analysis sug: subj: Emotional state Emotions Applied sciences Acquisition of data Video excerpts Sentiment analysis keyword: Psychology and Cognitive Sciences Psychology Sentiment and emotion modelling Speech Text Unimodal and multimodal labels Video ab: Emotion is a key characteristic that differentiates humans from machines. It is intricate, encompassing a wide variety of emotional states, and is expressed through both verbal and non-verbal communication channels. Different modalities contribute in unique ways to the integrated expression of emotion. However, in most of the existing multimodal datasets, there is only one unified emotion label for the various modalities, ignoring the heterogeneity and complementarity of the different modalities. To bridge this gap, we introduce UniC, a novel multimodal emotion dataset featuring both integrated multimodal labels and independent 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. We outline the steps taken to construct the dataset and analyze different modality perspectives in UniC. Our findings indicate that while in most cases the modality of text shares more emotional resemblance with the multimodal setup, other modalities can exhibit different, sometimes even opposite emotions that might contribute more to the overall emotion state. This dataset offers a modality-specific perspective on multimodal emotion analysis and has the potential to provide valuable insights for further research in human emotion understanding. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y custom: Language Resources & Evaluation is a copyright of Springer, 2025. All Rights Reserved. item: Language Resources & Evaluation holder: Springer Nature dt: @attributes: year: 2025 holdings: @attributes: islocal: N |
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