  As the key to sentiment analysis, sentiment composition considers the
classification of a constituent via classifications of its contained
sub-constituents and rules operated on them. Such compositionality has been
widely studied previously in the form of hierarchical trees including untagged
and sentiment ones, which are intrinsically suboptimal in our view. To address
this, we propose semantic tree, a new tree form capable of interpreting the
sentiment composition in a principled way. Semantic tree is a derivation of a
context-free grammar (CFG) describing the specific composition rules on
difference semantic roles, which is designed carefully following previous
linguistic conclusions. However, semantic tree is a latent variable since there
is no its annotation in regular datasets. Thus, in our method, it is
marginalized out via inside algorithm and learned to optimize the
classification performance. Quantitative and qualitative results demonstrate
that our method not only achieves better or competitive results compared to
baselines in the setting of regular and domain adaptation classification, and
also generates plausible tree explanations.
