  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 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; .4717 CCC for MuSe-Stress, and .4606 CCC for MuSe-Physio. For
MuSe-Sent an F1 score of 32.82 % is obtained.

Computation and Language;Artificial Intelligence