This chapter describes a new approach to dimensional MER that assigns soft (probabilistic) emotion values instead of hard (deterministic) emotion values to music pieces. This approach considers the affective content of a music signal as an emotion distribution in the emotion plane and trains a computational model to predict the music emotion distribution. In this way, one can model how subjective the perceived emotion of a music piece is and how likely a specific emotion (defined by valence and arousal values) would be perceived by a person while listening to the music piece. To our best knowledge, this is the first approach to dimensional MER that computes soft emotion values. This chapter also presents an extensive performance evaluation of this approach and describes how this approach can be applied to enhance our understanding of music emotion.