The MuSe 2021 Multimodal Sentiment Analysis Challenge: Sentiment, Emotion, Physiological-Emotion, and Stress
Lukas Stappen, Alice Baird, Lukas Christ, Lea Schumann, Benjamin, Sertolli, Eva-Maria Messner, Erik Cambria, Guoying Zhao, and Bj\"orn W., Schuller

TL;DR
MuSe 2021 is a comprehensive multimodal challenge integrating audio-visual, language, and biological signals to advance sentiment, emotion, and stress recognition with new datasets and baseline models.
Contribution
The challenge introduces four sub-tasks, new datasets including MuSe-CaR and Ulm-TSST, and provides baseline models for multimodal sentiment and emotion analysis.
Findings
Baseline CCC scores around 0.46-0.47 for emotion and stress prediction.
F1 score of 32.82% for sentiment classification.
Datasets capture user reviews and stressful depositions.
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 years' challenge, we utilise the MuSe-CaR dataset…
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Taxonomy
TopicsEmotion and Mood Recognition · Sentiment Analysis and Opinion Mining · Mental Health via Writing
