This chapter shows how probabilistic topic modeling drives interest in the study of topics, and how topic modeling has proven a successful tool for identifying coherent linguistic categories within collections of texts. Yet despite interest in topic models, no one has yet asked the question “What is a topic?” (either in classical rhetoric or computational study). If we derive large-scale semantic significance from texts, how does this relate to the longer philosophical/philological tradition? Beginning with an overview of the long (pre-computational) history of topics (from Aristotle to Renaissance commonplace books to nineteenth-century encyclopedism), then moving to a quantitative approach to topic modeling’s link to the past, the chapter uses a single topic (a single model run on a collection of German novels written over the course of the long nineteenth century) to explore larger metaphorical constellations associated with this topic (through the close reading of individual passages), and to apply a more quantitative approach (chapter 2’s method of multidimensional scaling, where word distributions are transformed into spatial representations). The topic’s semantic coherence (when it’s more or less present) at different states of likelihood within a text is compared to the spatial relationships and interconnectedness of topics (the way they coalesce/disperse).
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