It has been realized in the music emotion recognition (MER) community that personal difference, or individuality, has significant impact on the success of an MER system in practice. However, no previous work has explicitly taken individuality into consideration in an MER system. In this paper, the group-wise MER approach (GWMER) and personalized MER approach (PMER) are proposed to study the role of individuality. GWMER evaluates the importance of each individual factor such as sex, personality, and music experience, whereas PMER evaluates whether the prediction accuracy for a user is significantly improved if the MER system is personalized for the user. Experimental results demonstrate the effect of personalization and suggest the need for a better representation of individuality and for better prediction accuracy.