Sentiment analysis, especially for long documents, plausibly requires methods
capturing complex linguistics structures. To accommodate this, we propose a
novel framework to exploit task-related discourse for the task of sentiment
analysis. More specifically, we are combining the large-scale,
sentiment-dependent MEGA-DT treebank with a novel neural architecture for
sentiment prediction, based on a hybrid TreeLSTM hierarchical attention model.
Experiments show that our framework using sentiment-related discourse
augmentations for sentiment prediction enhances the overall performance for
long documents, even beyond previous approaches using well-established
discourse parsers trained on human annotated data. We show that a simple
ensemble approach can further enhance performance by selectively using
discourse, depending on the document length.

研究新架构用于情感分析的任务，结合了大规模的 MEGA-DT treebank 以及基于混合 TreeLSTM 分层注意模型的神经架构，通过情感相关的话语增强情感预测性能，实验结果显示，性能优于使用先前基于人类注释数据训练的良好建立的话语解析器的方法，同时演示了一个简单的组合方法如何进一步增强性能。