[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-160286-en":3,"doc-seo-160286-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},160286,8796095360427,"Lucas Martin","https://ap-avatar.wpscdn.com/davatar_994ba38a5ba835b3df7d355c54d3ed8d",6,"Technology","MuSe-Toolbox - The Multimodal Sentiment Analysis Continuous Annotation Fusion and Discrete Class Transformation Toolbox","MuSe-Toolbox is a Python-based open-source toolkit for building emotion gold standards in both continuous and discrete forms. A unified framework integrates multiple fusion methods and introduces Rater Aligned Annotation Weighting (RAAW), which aligns annotations in a translation-invariant way, then weights and fuses them using interrater agreement. The toolbox also supports exhaustive searches for meaningful class clusters in continuous gold standards. Experiments indicate it can produce better-discriminative class formations than hard-coded class boundaries with minimal manual effort, shipped out of the box via Docker.","MuSe-Toolbox: the multimodal sentiment analysis continuous annotation fusion and discrete class transformation toolbox  \nLukas Stappen, Lea Schumann, Benjamin Sertolli, Alice Baird, Benjamin Weigell, Erik Cambria, Björn W. Schuller  \nAngaben zur Veröffentlichung / Publication details:  \nStappen, Lukas, Lea Schumann, Benjamin Sertolli, Alice Baird, Benjamin Weigell, Erik Cambria, and Björn W. Schuller. 2021.“MuSe-Toolbox: the multimodal sentiment analysis continuous annotation fusion and discrete class transformation toolbox.” In MuSe ’ 21: Proceedings of the 2ndon Multimodal Sentiment Analysis Challenge, virtual event, China, 24 October 2021 , edited by Björn Schuller, Lukas Stappen, Eva-Maria Meßner, Erik Cambria, and Guoying Zhao, 75–82 . New York, NY: ACM. [https://doi.org/10.1145/3475957.3484451](https://doi.org/10.1145/3475957.3484451) .  \nNutzungsbedingungen / Terms of use: licgercopyright  \nDieses Dokument wird unter folgenden Bedingungen zur Verfügung gestellt: / This document is made available under these conditions:  \nDeutsches Urheberrecht  \nWeitere Informationen finden Sie unter: / For more information see:  \n[https://www.uni-augsburg. de/de/organisation/bibliothek/pub lizieren-zitieren-archivieren/publiz/](https://www.uni-augsburg. de/de/organisation/bibliothek/pub lizieren-zitieren-archivieren/publiz/)  \nMuSe-Toolbox: The Multimodal Sentiment Analysis Continuous Annotation Fusion and Discrete Class Transformation Toolbox  \nLukas Stappen  \nUniversity of Augsburg Augsburg, Germany  \nAlice Baird  \nUniversity of Augsburg Augsburg, Germany  \nLea Schumann  \nUniversity of Augsburg Augsburg, Germany  \nBenjamin Weigell  \nUniversity of Augsburg Augsburg, Germany  \nBenjamin Sertolli  \nUniversity of Augsburg Augsburg, Germany  \nErik Cambria  \nNanyang Technological University Singapore  \nBjörn W. Schuller Imperial College London London, United Kingdom  \nABSTRACT  \nWe introduce the MuSe-Toolbox – a Python-based open-source toolkit for creating a variety of continuous and discrete emotion gold standards. In a single framework, we unify a wide range of fusion methods and propose the novel Rater Aligned Annotation Weighting (RAAW ), which aligns the annotations in a translationinvariant way before weighting and fusing them based on the interrater agreements between the annotations. Furthermore, discrete categories tend to be easier for humans to interpret than continuous signals. With this in mind, the MuSe-Toolbox provides the functionality to run exhaustive searches for meaningful class clusters in the continuous gold standards. To our knowledge, this is the first toolkit that provides a wide selection of state-of-the-art emotional gold standard methods and their transformation to discrete classes. Experimental results indicate that MuSe-Toolbox can provide promising and novel class formations which can be better predicted than hard-coded classes boundaries with minimal human intervention. The implementation1 is out-of-the-box available with all dependencies using a Docker container2 .  \nCCS CONCEPTS  \n• Information systems → Multimedia and multimodal retrieval; • Computing methodologies → Artificial intelligence. KEYWORDS  \nAffective Computing; Annotation; Gold-Standard; Smoothing; Emotion classes; Emotion Recognition; Multimodal Sentiment Analysis  \n1[https://github.com/anonymous/MuSe-Toolbox](https://github.com/anonymous/MuSe-Toolbox)  \n[2](2 docker pull musetoolbox/musetoolbox)[ docker pull musetoolbox/musetoolbox](2 docker pull musetoolbox/musetoolbox)  \nThis is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in  \nMuSe ’21, October 24, 2021, Virtual Event, China  \n© 2021 Copyright held by the owner/author(s) . Publication rights licensed to ACM. ACM ISBN 978-1-4503-8678-4/21/10   $15 .00  \n[https://doi.org/10.1145/3475957.3484451](https://doi.org/10.1145/3475957.3484451)  \nACM Reference Format:  \nLukas Stappen, Lea Schumann, B","cbCaifLK7N0TznWz","https://ap.wps.com/l/cbCaifLK7N0TznWz","pdf",4054167,1,9,"English","en",105,"# Abstract\n# Introduction\n## Emotion AI and annotated reference data\n## Challenges in human rating and interrater disagreement\n## Goals addressed by MuSe-Toolbox","[{\"question\":\"What is MuSe-Toolbox used for?\",\"answer\":\"MuSe-Toolbox is a Python-based open-source toolkit for creating continuous and discrete emotion gold standards for multimodal sentiment analysis.\"},{\"question\":\"How does RAAW improve annotation fusion?\",\"answer\":\"RAAW aligns annotations in a translation-invariant way, then weights and fuses them according to interrater agreements before producing the fused gold standard.\"},{\"question\":\"How does MuSe-Toolbox support moving from continuous signals to discrete classes?\",\"answer\":\"It provides functionality to run exhaustive searches for meaningful class clusters within the continuous gold standards, generating emotion class formations with minimal human intervention.\"}]","MuSe-Toolbox - 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