## 6 Results and Discussion
### Group-Wise Classification Performance  
To answer RQ2, whether users' personality plays a role in the predictive modeling of music-induced emotion, we trained SVM classifiers on the datasets partitioned by participants' personality (see Table 1), with the procedure described in Section 3.2. Particularly, for the group-wise classifications, we only reported the performances in AUC for experiments with resampled training data, as (1) macro F1-score and AUC reflected similar patterns on the whole dataset; (2) the results of experiments with imbalanced or resampled training data revealed a similar pattern on the whole dataset; and (3) the models trained on resampled data showed more stable performances. Table 7 presents the classification performances (in AUC) of different feature modalities within each user personality group. Interestingly, compared to the generic model built on the whole (resampled) dataset, the group-wise models improved classification performances for some of the participant groups (indicated by \"+\" signs next to the performances in Table 7) but decreased prediction performances for the others (indicated by \"\(-\)\" signs in Table 7). For example, arousal classification within the extraversion (low) group and valence classification within the openness (high) group performed better than those in the whole dataset (Table 6), while arousal classification within the extraversion (high) group and valence classification within the conscientiousness (low) group performed worse. These observations imply that there might be some moderating effects of personality in individuals' emotional response to music. In other words, listeners with certain personality traits (e.g., less extraverted) may be more sensitive to acoustic features of music explored in this study and may have higher level physiological responses to music. This result is consistent with the results of [59] showing that extraversion is positively related to certain responses to music (e.g., happiness and sadness). This is also related to previous findings in psychology that introverted students tended to be more affected by background music due to relatively higher cortical arousal than extraverted students [60]. Group-wise classification might be helpful for modeling the effects of personality, which could be further verified in future investigations, preferably with larger samples. Similar to the results on the whole dataset, we also compared group-wise classification performances of different modalities, with results presented in Table 8. In group-wise _arousal_ classification experiments, the audio-only feature set consistently outperformed the physiology-only one, while the multimodal feature set did not significantly improve the performances of audio-only features in most cases (as indicated in the \"Aud. vs. Comb.\" column in Table 8). This is consistent with our finding on the whole dataset that the acoustic features were effective for arousal classification and the music modality alone was comparable with the multimodal approach (cf. Section 6.1). On the other hand, in group-wise _valence_ classification experiments, the combined feature set (i.e., audio + physiology) outperformed the audio-only feature set in all but two groups (i.e., openness to new experiences (low), extraversion (low)), and the improvement was statistically significant in the majority of cases (at \(p<0.001\), as indicated in the \"Aud. vs. Comb.\" column in Table 8). In particular, for the two user groups where the audio-only feature set achieved better performance in valence prediction, the audio-only feature set significantly outperformed the other two feature sets in the openness (low) group (at \(p<0.001\)), while, in the extraversion (low) group, the acoustic features outperformed the physiological features (at \(p<0.05\)), but the performance difference between the audio-only and the multimodal feature set was not statistically significant.