Dimensional MER involves an emotion annotation process that is more labor costly than that of its categorical counterpart. Subjects need to determine the numerical valence and arousal (VA) values of music pieces rather than assign emotion labels to them. The heavy cognitive load of emotion annotation impedes the collection of large-scale ground truth annotations and also harms the reliability of the annotations. Because the generality of the training instances (which is related to the size of the training data set) and the quality of ground truth annotations are essential to the performance of a machine learning model, reducing the effort of emotion annotation plays a key role in the progress of dimensional MER. This chapter provides the details of a ranking approach that resolves this issue.