Proceedings of the 2nd on Multimodal Sentiment Analysis Challenge最新文献

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MuSe-Toolbox: The Multimodal Sentiment Analysis Continuous Annotation Fusion and Discrete Class Transformation Toolbox MuSe-Toolbox:多模态情感分析、连续注释融合和离散类转换工具箱

Proceedings of the 2nd on Multimodal Sentiment Analysis Challenge Pub Date : 2021-07-25 DOI: 10.1145/3475957.3484451

Lukas Stappen, Lea Schumann, Benjamin Sertolli, Alice Baird, B. Weigel, E. Cambria, Björn Schuller

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引用次数: 27

The MuSe 2021 Multimodal Sentiment Analysis Challenge: Sentiment, Emotion, Physiological-Emotion, and Stress MuSe 2021多模态情感分析挑战:情感、情感、生理情感和压力

Proceedings of the 2nd on Multimodal Sentiment Analysis Challenge Pub Date : 2021-04-14 DOI: 10.1145/3475957.3484450

Lukas Stappen, Alice Baird, Lukas Christ, Lea Schumann, Benjamin Sertolli, Eva-Maria Messner, E. Cambria, Guoying Zhao, B. Schuller

{"title":"The MuSe 2021 Multimodal Sentiment Analysis Challenge: Sentiment, Emotion, Physiological-Emotion, and Stress","authors":"Lukas Stappen, Alice Baird, Lukas Christ, Lea Schumann, Benjamin Sertolli, Eva-Maria Messner, E. Cambria, Guoying Zhao, B. Schuller","doi":"10.1145/3475957.3484450","DOIUrl":"https://doi.org/10.1145/3475957.3484450","url":null,"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.","PeriodicalId":313996,"journal":{"name":"Proceedings of the 2nd on Multimodal Sentiment Analysis Challenge","volume":"77 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116357618","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 56

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