  We propose SentiBERT, a variant of BERT that effectively captures
compositional sentiment semantics. The model incorporates contextualized
representation with binary constituency parse tree to capture semantic
composition. Comprehensive experiments demonstrate that SentiBERT achieves
competitive performance on phrase-level sentiment classification. We further
demonstrate that the sentiment composition learned from the phrase-level
annotations on SST can be transferred to other sentiment analysis tasks as well
as related tasks, such as emotion classification tasks. Moreover, we conduct
ablation studies and design visualization methods to understand SentiBERT. We
show that SentiBERT is better than baseline approaches in capturing negation
and the contrastive relation and model the compositional sentiment semantics.
