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Discourse structure is the hidden link between surface features anddocument-level properties, such as sentiment polarity. We show that thediscourse analyses produced by Rhetorical Structure Theory (RST) parsers canimprove document-level sentiment analysis, via composition of local informationup the discourse tree. First, we show that reweighting discourse unitsaccording to their position in a dependency representation of the rhetoricalstructure can yield substantial improvements on lexicon-based sentimentanalysis. Next, we present a recursive neural network over the RST structure,which offers significant improvements over classification-based methods.

Better Document-level Sentiment Analysis from RST Discourse Parsing