Better Document-level Sentiment Analysis from RST Discourse ParsingCode is available at https://github.com/parry2403/R2N2
Table 1: Sentiment classification accuracies on two movie review datasets [pang2004sentimental, socher2013recursive], described in Section 2.3.
['[EMPTY]', 'Pang & Lee', 'Socher et al.']
As seen in the comparison between lines B1 and D1, discourse depth weighting offers substantial improvements over the bag-of-words approach for lexicon-based sentiment analysis, with raw improvements of 4−5%. Given the simplicity of this approach — which requires only a sentiment lexicon and a discourse parser — we strongly recommend the application of discourse depth weighting for lexicon-based sentiment analysis at the document level. However, the improvements for the classification-based models are considerably smaller, less than 1% in both datasets. Even without distinguishing between discourse relations, we get an improvement of more than 3% accuracy on the Stanford data, and 0.5% on the smaller Pang & Lee dataset. Adding sensitivity to discourse relations (distinguishing K(r) for contrastive and non-contrastive relations) offers further improvements on the Pang & Lee data, outperforming the baseline classifier (D2) by 1.3%. There are unfortunately few possibilities for direct comparison of our approach against prior work. \newciteheerschop2011polarity and \newcitewachsmuth2014modeling also employ the \newcitepang2004sentimental dataset, but neither of their results are directly comparable: \newciteheerschop2011polarity exclude documents that SPADE fails to parse, and \newcitewachsmuth2014modeling evaluates only on individual sentences rather than entire documents. They evaluate on the Pang and Lee data, and consider only lexicon-based sentiment analysis, obtaining document-level accuracies between 65% (for the baseline) and 72% (for their best discourse-augmented system).