  Prominent applications of sentiment analysis are countless, covering areas
such as marketing, customer service and communication. The conventional
bag-of-words approach for measuring sentiment merely counts term frequencies;
however, it neglects the position of the terms within the discourse. As a
remedy, we develop a discourse-aware method that builds upon the discourse
structure of documents. For this purpose, we utilize rhetorical structure
theory to label (sub-)clauses according to their hierarchical relationships and
then assign polarity scores to individual leaves. To learn from the resulting
rhetorical structure, we propose a tensor-based, tree-structured deep neural
network (named Discourse-LSTM) in order to process the complete discourse tree.
The underlying tensors infer the salient passages of narrative materials. In
addition, we suggest two algorithms for data augmentation (node reordering and
artificial leaf insertion) that increase our training set and reduce
overfitting. Our benchmarks demonstrate the superior performance of our
approach. Moreover, our tensor structure reveals the salient text passages and
thereby provides explanatory insights.
