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We explore the structure of meanings in human language through the analysis of colexification networks, developing methods to automatically improve affective science resources. We prove that affective meaning is encoded in colexification networks and deploy such networks to improve state of the art methods to infer the affective ratings of words. This constitutes a first step towards explainable and theory-based methods for text analysis and automatic expansion of lexica.
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