  Embeddings of words and concepts capture syntactic and semantic regularities
of language; however, they have seen limited use as tools to study
characteristics of different corpora and how they relate to one another. We
introduce TextEssence, an interactive system designed to enable comparative
analysis of corpora using embeddings. TextEssence includes visual,
neighbor-based, and similarity-based modes of embedding analysis in a
lightweight, web-based interface. We further propose a new measure of embedding
confidence based on nearest neighborhood overlap, to assist in identifying
high-quality embeddings for corpus analysis. A case study on COVID-19
scientific literature illustrates the utility of the system. TextEssence is
available from https://github.com/drgriffis/text-essence.
