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.

翻译：情感组合作为情感分析的关键，通过所含子成分的分类及其操作规则来确定成分的分类。这种组合性此前广泛以层级树形式进行研究，包括非标记树和情感树，但从本质上看这些形式存在次优性。为解决此问题，我们提出语义树——一种能够以原则性方式解释情感组合的新型树结构。语义树是基于上下文无关文法（CFG）的推导，该文法精确描述了不同语义角色上的组合规则，并依据前人语言学结论精心设计。然而，由于常规数据集中缺乏语义树标注，它被视为潜在变量。因此，在我们的方法中，通过内部算法对其进行边缘化处理，并学习优化分类性能。定量与定性结果表明，我们的方法不仅在常规分类和领域自适应分类中达到优于或可竞争基线的效果，还能生成合理的树形解释。