  Neural models have been investigated for sentiment classification over
constituent trees. They learn phrase composition automatically by encoding tree
structures but do not explicitly model sentiment composition, which requires to
encode sentiment class labels. To this end, we investigate two formalisms with
deep sentiment representations that capture sentiment subtype expressions by
latent variables and Gaussian mixture vectors, respectively. Experiments on
Stanford Sentiment Treebank (SST) show the effectiveness of sentiment grammar
over vanilla neural encoders. Using ELMo embeddings, our method gives the best
results on this benchmark.
