Recognition of emotional states from other’s actions is one of key capability for smooth social interaction. The present study provides a computational-theory-level analysis on which feature may take a crucial role in recognition of emotional attributes in human actions represented as point-light display. Lead by the previous theoretical works and empirical findings, the velocity and acceleration profile was investigated as a major feature of emotional attributes classification. The results showed that emotional attributes in actions as well as action types could be identified by covariance of velocity profiles among multiple body parts. Since, despite different velocity profiles in different actions, these features for emotional attributes were found commonly in multiple different actions, it suggests that the action styles may be mediated by an information channel parallel to action types per se.