Taboada et al. (2008) propose a word-based method for extracting sentiment from text that relies on the most relevant parts of a text. The method predicts that opinion words found in the nuclei (more important parts) of a document are more significant for the overall sentiment, whereas opinion words found in the satellites (less important parts) only potentially interfere with the overall sentiment. However, as pointed out by Taboada et al. (2008) and Narayanan et al. (2009), for certain discourse relations (for instance, Condition relations), the calculation of sentiment should involve both parts of the relation. Based on our analysis of the affective content expressed by automatically extracted discourse relations from the Simon Fraser University Corpus (Taboada 2008) and the Penn Discourse Treebank (Prasad et al. 2008), we propose to classify all the discourse relations into four categories: (1) relations that reverse polarity, (2) intensify polarity, (3) downtone polarity, or (4) produce no change in polarity. We compare the performance of a sentiment analysis system (SO-CAL, Taboada et al. 2011) when opinion words are detected only in the nuclei with its performance when both parts of the relation are analyzed in combination with the opinion words. The results of the experiment show that extraction of both the nucleus and the satellite parts of texts does not improve the performance of a sentiment extraction system.