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Emotions are expressed in nuanced ways, which varies by collective or individual experiences, knowledge, and beliefs. Therefore, to understand emotion, as conveyed through text, a robust mechanism capable of capturing and modeling different linguistic nuances and phenomena is needed. We propose a semisupervised, graph-based algorithm to produce rich structural descriptors which serve as the building blocks for constructing contextualized affect representations from text. The pattern-baseddoi:10.18653/v1/d18-1404 dblp:conf/emnlp/SaraviaLHWC18 fatcat:k646kcqednegxnqxmcd2spdyum