The manipulation of different interpretational factors, including dynamics, duration, and vibrato, constitutes the realization of different expressions in music. Therefore, a deeper understanding of the workings of these factors is critical for advanced expressive synthesis and computer-aided music education. In this paper, we propose the novel task of automatic expressive musical term classification as a direct means to study the interpretational factors. Specifically, we consider up to 10 expressive musical terms, such as Scherzando and Tranquillo, and compile a new dataset of solo violin excerpts featuring the realization of different expressive terms by different musicians for the same set of classical music pieces. Under a score-informed scheme, we design and evaluate a number of note-level features characterizing the interpretational aspects of music for the classification task. Our evaluation shows that the proposed features lead to significantly higher classification accuracy than a baseline feature set commonly used in music information retrieval tasks. Moreover, taking the contrast of feature values between an expressive and its corresponding non-expressive version (if given) of a music piece greatly improves the accuracy in classifying the presented expressive one. We also draw insights from analyzing the feature relevance and the class-wise accuracy of the prediction.