  The Multimodal Sentiment Analysis Challenge (MuSe) 2022 is dedicated to
multimodal sentiment and emotion recognition. For this year's challenge, we
feature three datasets: (i) the Passau Spontaneous Football Coach Humor
(Passau-SFCH) dataset that contains audio-visual recordings of German football
coaches, labelled for the presence of humour; (ii) the Hume-Reaction dataset in
which reactions of individuals to emotional stimuli have been annotated with
respect to seven emotional expression intensities, and (iii) the Ulm-Trier
Social Stress Test (Ulm-TSST) dataset comprising of audio-visual data labelled
with continuous emotion values (arousal and valence) of people in stressful
dispositions. Using the introduced datasets, MuSe 2022 2022 addresses three
contemporary affective computing problems: in the Humor Detection Sub-Challenge
(MuSe-Humor), spontaneous humour has to be recognised; in the Emotional
Reactions Sub-Challenge (MuSe-Reaction), seven fine-grained `in-the-wild'
emotions have to be predicted; and in the Emotional Stress Sub-Challenge
(MuSe-Stress), a continuous prediction of stressed emotion values is featured.
The challenge is designed to attract different research communities,
encouraging a fusion of their disciplines. Mainly, MuSe 2022 targets the
communities of audio-visual emotion recognition, health informatics, and
symbolic sentiment analysis. This baseline paper describes the datasets as well
as the feature sets extracted from them. A recurrent neural network with LSTM
cells is used to set competitive baseline results on the test partitions for
each sub-challenge. We report an Area Under the Curve (AUC) of .8480 for
MuSe-Humor; .2801 mean (from 7-classes) Pearson's Correlations Coefficient for
MuSe-Reaction, as well as .4931 Concordance Correlation Coefficient (CCC) and
.4761 for valence and arousal in MuSe-Stress, respectively.
