Musical statements can be interpreted in performance with a wide variety of stylistic and expressive inflections. We explore how different musical characters are performed based on an extension of the basis function models, a data-driven framework for expressive performance. In this framework, expressive dimensions such as tempo, dynamics and articulation are modeled as a function of score features, i.e. numerical encodings of specific aspects of a musical score, using neural networks. By allowing the user to weight the contribution of the input score features, we show that predictions of expressive dimensions can be used to express different musical characters