Print Email Facebook Twitter Annotation practices in affective computing Title Annotation practices in affective computing: What are these algorithms actually trained on? Author Backer, Suzanne (TU Delft Electrical Engineering, Mathematics and Computer Science) Contributor Liem, C.C.S. (mentor) Demetriou, A.M. (mentor) Broz, F. (graduation committee) Degree granting institution Delft University of Technology Programme Computer Science and Engineering Project CSE3000 Research Project Date 2023-06-28 Abstract In the machine learning research community, significant importance is given to the optimization of techniques which are employed once a benchmark dataset is given. However, less importance is assigned to the quality of these datasets and to how these datasets are obtained. In this work, we look into annotation practices in the research area of affective computing, analysing datasets of emotion classification tasks from text, video, audio, EEG data and more. We find annotation practices of varying quality and recommend that annotation practices be improved, especially with regard to multiple annotator overlap. Subject Affective ComputingAnnotation PracticesMachine Learning To reference this document use: http://resolver.tudelft.nl/uuid:3a999106-c31d-4e3d-9a22-a462bf8b25a1 Part of collection Student theses Document type bachelor thesis Rights © 2023 Suzanne Backer Files PDF CSE3000_Final_Paper.pdf 188.56 KB Close viewer /islandora/object/uuid:3a999106-c31d-4e3d-9a22-a462bf8b25a1/datastream/OBJ/view