This paper proposes an emotion detection method using a combination of dimensional approach and categorical approach. Thayer's model is divided into discrete emotion sections based on the level of arousal and valence. The main objective of the method is to increase the number of detected emotions which is used for emotion visualization. To evaluate the suggested method, we conducted various experiments with supervised learning and feature selection strategies. We collected 300 music clips with emotions annotated by music experts. Two feature sets are employed to create two training models for arousal and valence dimensions of Thayer's model. Finally, 36 music emotions are detected by proposed method. The results showed that the suggested algorithm achieved the highest accuracy when using RandomForest classifier with 70% and 57.3% for arousal and valence, respectively. These rates are better than previous studies.