This tool exploits the fact that topic modeling can be used to generalize about topics on an aggregate level as well as a fine-grained level, an approach that has the consequences of revealing overarching themes that appear across all texts as well as more idosyncratic events and rehtorical styles that adhere to only a few documents. In this article, we introduce the concept of “nested topics,” an approach to topic modeling large-scale textual corpora that highlights implicit ontologies and relationships within the texts themselves. Scholars need tools that will allow them to generalize about the fit of themes, events, and rhetorical styles represented in a body of texts.
