Better Document-level Sentiment Analysis from RST Discourse Parsing
arXiv:1509.01599
Abstract
Discourse structure is the hidden link between surface features and document-level properties, such as sentiment polarity. We show that the discourse analyses produced by Rhetorical Structure Theory (RST) parsers can improve document-level sentiment analysis, via composition of local information up the discourse tree. First, we show that reweighting discourse units according to their position in a dependency representation of the rhetorical structure can yield substantial improvements on lexicon-based sentiment analysis. Next, we present a recursive neural network over the RST structure, which offers significant improvements over classification-based methods.
Published at Empirical Methods in Natural Language Processing (EMNLP 2015)
Cited by in corpus (6)
- Aspect Level Sentiment Classification with Deep Memory Network
- Neural Discourse Structure for Text Categorization
- Learning Structured Text Representations
- Cached Long Short-Term Memory Neural Networks for Document-Level Sentiment Classification
- Multiple Instance Learning Networks for Fine-Grained Sentiment Analysis
- Summarizing Opinions: Aspect Extraction Meets Sentiment Prediction and They Are Both Weakly Supervised