The MuSe 2021 multimodal sentiment analysis challenge : sentiment, emotion, physiological-emotion, and stress
Stappen, Lukas; Baird, Alice; Christ, Lukas; Schumann, Lea; Sertolli, Benjamin; Messner, Eva-Maria; Cambria, Erik; Zhao, Guoying; Schuller, Björn W. (2021-10-20)
Lukas Stappen, Alice Baird, Lukas Christ, Lea Schumann, Benjamin Sertolli, Eva-Maria Meßner, Erik Cambria, Guoying Zhao, and Björn W. Schuller. 2021. The MuSe 2021 Multimodal Sentiment Analysis Challenge: Sentiment, Emotion, Physiological-Emotion, and Stress. Proceedings of the 2nd on Multimodal Sentiment Analysis Challenge. Association for Computing Machinery, New York, NY, USA, 5–14. DOI:https://doi.org/10.1145/3475957.3484450
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https://urn.fi/URN:NBN:fi-fe2022022420753
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Abstract
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 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.
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