Sentiment-Based Ranking of Blog Posts using Rhetorical Structure Theory

Polarity estimation in large-scale and multi-topic domains is a difficult issue. Most state-of-the-art solutions essentially rely on frequencies of sentiment-carrying words (e.g., taken from a lexicon) when analyzing the sentiment conveyed by natural language text. These approaches ignore the structural aspects of a document, which contain valuable information. Rhetorical Structure Theory (RST) provides important information about the relative importance of the different text spans in a document. This knowledge could be useful for sentiment analysis and polarity classification. However, RST has only been studied for polarity classification problems in constrained and small scale scenarios. The main objective of this paper is to explore the usefulness of RST in large-scale polarity ranking of blog posts. We apply sentence-level methods to select the key sentences that convey the overall on-topic sentiment of a blog post. Then, we apply RST analysis to these core sentences in order to guide the classification of their polarity and thus to generate an overall estimation of the document's polarity with respect to a specific topic. Our results show that RST provides valuable information about the discourse structure of the texts that can be used to make a more accurate ranking of documents in terms of their estimated sentiment in multi-topic blogs.