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.

本研究通过对语音、视觉、语言和生物信号进行综合集成，聚焦于情感和情绪、生理情感和基于情感的压力识别的挑战，并介绍了包括情感识别和健康信息学在内的不同领域的社区。同时，介绍了通过本研究提供的基线模型，长短时记忆-递归神经网络，从使用者生成的评论数据集MuSe-CaR和展示压力状态下人们的Ulm-TSST数据集中提取的基于最先进技术的特征集。在每个子挑战中，我们对参与者进行一项竞争性基线测试，并在测试中报告了MuSe-Wilder的0.4616CCC，MuSe-Stress的0.4717CCC和MuSe-Physio的0.4606CCC的合一性相关系数以及MuSe-Sent的32.82%的F1分数。

MuSe 2021多模态情感分析挑战赛：情感、情绪、生理情感和压力