Acquiring Background Knowledge to Improve Moral Value Prediction

We address the problem of detecting expressions of moral values in tweets using content analysis. This is a particularly challenging problem because moral values are often only implicitly signaled in language, and tweets contain little contextual information due to length constraints. To address these obstacles, we present a novel approach to automatically acquire background knowledge from an external knowledge base to enrich input texts and thus improve moral value prediction. By combining basic textual features with background knowledge, our overall context-aware framework achieves performance comparable to a single human annotator. Our approach obtains 13.3% absolute F -score gains compared to our baseline model that only uses textual features.