In recent years, advancements in artificial intelligence have driven a growing demand for personalized user experiences across various digital platforms. In the music domain, this trend is reflected in the need for more sophisticated recommendation systems beyond traditional collaborative filtering methods. This thesis introduces an emotion-based multimodal music classifier, leveraging both audio features and song lyrics to capture the emotional content of music. By focusing on song content and emotional attributes, this approach aims to lay the groundwork for recommendation systems capable of providing users with a more customized and emotionally resonant experience, also addressing the cold-start problem typical of collaborative filtering-based recommendation.