This thesis explores how explainability can be introduced into Music Emo- tion Recognition (MER) models, which are usually hard to interpret despite their good performance. While many deep learning models can predict the emotional content of music with high accuracy, they often work as black boxes, giving little to no information about how they reach their conclusions. The goal of this work is to make these models more understandable, especially for users who might want to exploit them not just as tools, but also to learn something from them. To do this, the thesis develops and tests two different approaches. The first one is based on musical features—some taken from the literature, and others introduced as a novel contribution.