  Using the frequency of keywords is a classic approach in the formal analysis
of text, but has the drawback of glossing over the relationality of word
meanings. Word embedding models overcome this problem by constructing a
standardized and continuous "meaning space" where words are assigned a location
based on relations of similarity to other words based on how they are used in
natural language samples. We show how word embeddings are commensurate with
prevailing theories of meaning in sociology and can be put to the task of
interpretation via two kinds of navigation. First, one can hold terms constant
and measure how the embedding space moves around them--much like astronomers
measured the changing of celestial bodies with the seasons. Second, one can
also hold the embedding space constant and see how documents or authors move
relative to it--just as ships use the stars on a given night to determine their
location. Using the empirical case of immigration discourse in the United
States, we demonstrate the merits of these two broad strategies for advancing
important topics in cultural theory, including social marking, media fields,
echo chambers, and cultural diffusion and change more broadly.
