Learning from Machines: Differentiating US Presidential Campaigns with Attribution and Annotation
Identifying the differing ways in which political actors and groups express themselves is a key task in the study of legislatures, campaigning, and communication. A variety of computational tools exist to help find and describe these patterns, typically summarizing differences with weighted word lists representing either lexical frequencies or semantic fields. I identify two limits to the inferences that can be made based on this method: the ambiguity of the semantic value of words without wider context and an inability to detect differences outside of lexical semantics.