From Sentiment Annotations to Sentiment Prediction through Discourse Augmentation

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