This research delves into the exploration of translation methods between affect representation schemes within the domain of text content analysis. We assess their performance on various affect analysis tasks while concurrently developing a robust evaluation framework. Furthermore, we collect annotated datasets and take into account crucial contextual and individual factors. Ultimately, our goal is to contribute to the advancement of powerful and sophisticated tools for affect analysis. We believe a successful automated translation will aid in achieving a more comprehensive and rounded understanding of affect and further research in different fields, such as psychology and sociology.