Summary of MuSe 2020: Multimodal Sentiment Analysis, Emotion-target Engagement and Trustworthiness Detection in Real-life Media

Lukas Stappen, Björn Schuller, Iulia Lefter, Erik Cambria, Ioannis Kompatsiaris

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

11 Scopus citations

Abstract

The first Multimodal Sentiment Analysis in Real-life Media (MuSe) 2020 was a Challenge-based Workshop held in conjunction with ACM Multimedia'20. It addresses three distinct 'in-the-wild' Sub-challenges: sentiment/ emotion recognition (MuSe-Wild), emotion-target engagement (MuSe-Target) and trustworthiness detection (MuSe-Trust). A large multimedia dataset MuSe-CaR was used, which was specifically designed with the intention of improving machine understanding approaches of how sentiment (e.g. emotion) is linked to a topic in emotional, user-generated reviews. In this summary, we describe the motivation, first of its kind 'in-the-wild' database, challenge conditions, participation, as well as giving an overview of utilised state-of-the-art techniques.

Original languageEnglish
Title of host publicationMM 2020 - Proceedings of the 28th ACM International Conference on Multimedia
PublisherAssociation for Computing Machinery, Inc
Pages4769-4770
Number of pages2
ISBN (Electronic)9781450379885
DOIs
StatePublished - 12 Oct 2020
Externally publishedYes
Event28th ACM International Conference on Multimedia, MM 2020 - Virtual, Online, United States
Duration: 12 Oct 202016 Oct 2020

Publication series

NameMM 2020 - Proceedings of the 28th ACM International Conference on Multimedia

Conference

Conference28th ACM International Conference on Multimedia, MM 2020
Country/TerritoryUnited States
CityVirtual, Online
Period12/10/2016/10/20

Keywords

  • affective computing
  • multimodal fusion
  • multimodal sentiment analysis
  • user-generated data

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