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.