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.