DeepAI AI Chat
Log In Sign Up

MuSe 2020 – The First International Multimodal Sentiment Analysis in Real-life Media Challenge and Workshop

by   Lukas Stappen, et al.

Multimodal Sentiment Analysis in Real-life Media (MuSe) 2020 is a Challenge-based Workshop focusing on the tasks of sentiment recognition, as well as emotion-target engagement and trustworthiness detection by means of more comprehensively integrating the audio-visual and language modalities. The purpose of MuSe 2020 is to bring together communities from different disciplines; mainly, the audio-visual emotion recognition community (signal-based), and the sentiment analysis community (symbol-based). We present three distinct sub-challenges: MuSe-Wild, which focuses on continuous emotion (arousal and valence) prediction; MuSe-Topic, in which participants recognise domain-specific topics as the target of 3-class (low, medium, high) emotions; and MuSe-Trust, in which the novel aspect of trustworthiness is to be predicted. In this paper, we provide detailed information on MuSe-CaR, the first of its kind in-the-wild database, which is utilised for the challenge, as well as the state-of-the-art features and modelling approaches applied. For each sub-challenge, a competitive baseline for participants is set; namely, on test we report for MuSe-Wild a combined (valence and arousal) CCC of .2568, for MuSe-Topic a score (computed as 0.34· UAR + 0.66·F1) of 76.78 the 10-class topic and 40.64 MuSe-Trust a CCC of .4359.


page 1

page 2

page 3

page 4


The MuSe 2021 Multimodal Sentiment Analysis Challenge: Sentiment, Emotion, Physiological-Emotion, and Stress

Multimodal Sentiment Analysis (MuSe) 2021 is a challenge focusing on the...

The Multimodal Sentiment Analysis in Car Reviews (MuSe-CaR) Dataset: Collection, Insights and Improvements

Truly real-life data presents a strong, but exciting challenge for senti...

MUSE2020 challenge report

This paper is a brief report for MUSE2020 challenge. We present our solu...

Fusing Audio, Textual and Visual Features for Sentiment Analysis of News Videos

This paper presents a novel approach to perform sentiment analysis of ne...

EmotiW 2018: Audio-Video, Student Engagement and Group-Level Affect Prediction

This paper details the sixth Emotion Recognition in the Wild (EmotiW) ch...

Variants of BERT, Random Forests and SVM approach for Multimodal Emotion-Target Sub-challenge

Emotion recognition has become a major problem in computer vision in rec...

Code Repositories


Accompany code to reproduce the baselines of the International Multimodal Sentiment Analysis Challenge (MuSe 2020).

view repo