Use of Discourse Knowledge to Improve Lexicon-based Sentiment Analysis

Abstract

Sentiment Analysis deals with the computational treatment of sentiment in texts. Discourse is a linguistic level of analysis where the author represents ideas and links concepts in a rational chain of thoughts. One important representation of discourse is the Rhetorical Structure Theory (RST). The objective of this work consists in to use discourse knowledge to improve a lexicon-based sentiment classifier. To achieve this goal it presents a lexicon- based algorithm adapted to weight portions of text under particular RST relations distinctly. Two experiments are reported. The first experiment verifies if the RST improves sentiment classification. It also shows the discourse relations which are most important in the process. The second experiment incorporates discourse markers in the algorithm in order to eliminate the necessity of a RST annotated corpus. It uses the weights learned in the first experiment to perform the sentiment classification.