- Multimodal Sentiment Analysis (MuSe) 2021 is a challenge focusing on the tasks of sentiment and emotion, as well as physiological-emotion and emotion-based stress recognition through more comprehensively integrating the audio-visual, language, and biological signal modalities. The purpose of MuSe 2021 is to bring together communities from different disciplines; mainly, the audio-visual emotion recognition community (signal-based), the sentiment analysis community (symbol-based), and the health informatics community. We present four distinct sub-challenges: MuSe-Wilder and MuSe-Stress which focus on continuous emotion (valence and arousal) prediction; MuSe-Sent, in which participants recognise five classes each for valence and arousal; and MuSe-Physio, in which the novel aspect of 'physiological-emotion' is to be predicted. For this year's challenge, we utilise the MuSe-CaR dataset focusing on user-generated reviews and introduce the Ulm-TSST dataset, which displays people in stressfulMultimodal Sentiment Analysis (MuSe) 2021 is a challenge focusing on the tasks of sentiment and emotion, as well as physiological-emotion and emotion-based stress recognition through more comprehensively integrating the audio-visual, language, and biological signal modalities. The purpose of MuSe 2021 is to bring together communities from different disciplines; mainly, the audio-visual emotion recognition community (signal-based), the sentiment analysis community (symbol-based), and the health informatics community. We present four distinct sub-challenges: MuSe-Wilder and MuSe-Stress which focus on continuous emotion (valence and arousal) prediction; MuSe-Sent, in which participants recognise five classes each for valence and arousal; and MuSe-Physio, in which the novel aspect of 'physiological-emotion' is to be predicted. For this year's challenge, we utilise the MuSe-CaR dataset focusing on user-generated reviews and introduce the Ulm-TSST dataset, which displays people in stressful depositions. This paper also provides detail on the state-of-the-art feature sets extracted from these datasets for utilisation by our baseline model, a Long Short-Term Memory-Recurrent Neural Network. For each sub-challenge, a competitive baseline for participants is set; namely, on test, we report a Concordance Correlation Coefficient (CCC) of .4616 CCC for MuSe-Wilder; .5088 CCC for MuSe-Stress, and .4908 CCC for MuSe-Physio. For MuSe-Sent an F1 score of 32.82% is obtained.…
MetadatenAuthor: | Lukas Stappen, Alice BairdGND, Lukas Christ, Lea Schumann, Benjamin Sertolli, Eva-Maria Meßner, Erik Cambria, Guoying Zhao, Björn W. SchullerORCiDGND |
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URN: | urn:nbn:de:bvb:384-opus4-992830 |
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Frontdoor URL | https://opus.bibliothek.uni-augsburg.de/opus4/99283 |
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ISBN: | 978-1-4503-8678-4OPAC |
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Parent Title (English): | Proceedings of the 2nd on Multimodal Sentiment Analysis Challenge, Virtual Event, China, 24 October 2021 |
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Publisher: | ACM |
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Place of publication: | New York, NY |
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Editor: | Björn W. SchullerORCiDGND, Lukas Stappen, Eva-Maria Meßner, Erik Cambria, Guoying Zhao |
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Type: | Conference Proceeding |
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Language: | English |
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Year of first Publication: | 2021 |
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Publishing Institution: | Universität Augsburg |
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Release Date: | 2022/11/16 |
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First Page: | 5 |
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Last Page: | 14 |
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DOI: | https://doi.org/10.1145/3475957.3484450 |
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Institutes: | Fakultät für Angewandte Informatik |
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| Fakultät für Angewandte Informatik / Institut für Informatik |
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| Fakultät für Angewandte Informatik / Institut für Informatik / Lehrstuhl für Embedded Intelligence for Health Care and Wellbeing |
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Dewey Decimal Classification: | 0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 004 Datenverarbeitung; Informatik |
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Licence (German): | Deutsches Urheberrecht |
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