Print Email Facebook Twitter Exploring Automatic Translation between Affect Representation Schemes Title Exploring Automatic Translation between Affect Representation Schemes: Video Affective Content Analysis Author Dimitrov, Ivan (TU Delft Electrical Engineering, Mathematics and Computer Science) Contributor Dudzik, B.J.W. (mentor) Raman, C.A. (mentor) Hanjalic, A. (graduation committee) Degree granting institution Delft University of Technology Programme Computer Science and Engineering Project CSE3000 Research Project Date 2023-06-25 Abstract The objective of this report is to establish and present a machine learning model that effectively translates affect representation from emotional attributes such as arousal (passive versus active) and valence (negative versus positive) to dominance (weak versus strong). In the pursuit of this goal, various research questions are addressed. The paper outlines the process of dataset selection, ensuring appropriateness for the problem at hand. Subsequently, a comprehensive investigation into suitable evaluation methods for the developed model is conducted, providing well-reasoned justifications for the chosen approach. An additional research question focuses on assessing different machine learning approaches to determine the optimal performer. The motivation behind this translation lies in the recognition of the interdependence between these affect attributes, supported by both theoretical underpinnings and practical evidence. This contrasts with previous studies that have treated these dimensions as independent descriptors for representing emotions. To reference this document use: http://resolver.tudelft.nl/uuid:55c84c34-5684-41f1-830b-96eabfae23a7 Part of collection Student theses Document type bachelor thesis Rights © 2023 Ivan Dimitrov Files PDF Paper.pdf 527.59 KB Close viewer /islandora/object/uuid:55c84c34-5684-41f1-830b-96eabfae23a7/datastream/OBJ/view