We investigated the relationship between high-level chord features and the perceived semantic and emotional expression of musical pieces in the context of music branding. Therefore, we first developed high-level chord features based on musicological considerations and novel MIR technologies. Inter alia, these features represent the number of chords, the proportion of major/minor chords, and the frequency of certain cadences and turnarounds. The validity of these features for predicting listeners' perceived musical expression beyond genre information was subsequently tested by means of data from two online listening experiments, where musical expression of 549 music titles had been rated on four factors, Easy-going, Joyful, Authentic , and Progressive. Results show that in all four models chord features significantly improved prediction results. Most important features turned out to be those representing the number of (unique) chords and the proportion of minor chords. Implications of results are discussed, and future work is outlined.