  Prosodic phrasing is crucial to the naturalness and intelligibility of
end-to-end Text-to-Speech (TTS). There exist both linguistic and emotional
prosody in natural speech. As the study of prosodic phrasing has been
linguistically motivated, prosodic phrasing for expressive emotion rendering
has not been well studied. In this paper, we propose an emotion-aware prosodic
phrasing model, termed \textit{EmoPP}, to mine the emotional cues of utterance
accurately and predict appropriate phrase breaks. We first conduct objective
observations on the ESD dataset to validate the strong correlation between
emotion and prosodic phrasing. Then the objective and subjective evaluations
show that the EmoPP outperforms all baselines and achieves remarkable
performance in terms of emotion expressiveness. The audio samples and the code
are available at \url{https://github.com/AI-S2-Lab/EmoPP}.
