  Human language is colored by a broad range of topics, but existing text
analysis tools only focus on a small number of them. We present Empath, a tool
that can generate and validate new lexical categories on demand from a small
set of seed terms (like "bleed" and "punch" to generate the category violence).
Empath draws connotations between words and phrases by deep learning a neural
embedding across more than 1.8 billion words of modern fiction. Given a small
set of seed words that characterize a category, Empath uses its neural
embedding to discover new related terms, then validates the category with a
crowd-powered filter. Empath also analyzes text across 200 built-in,
pre-validated categories we have generated from common topics in our web
dataset, like neglect, government, and social media. We show that Empath's
data-driven, human validated categories are highly correlated (r=0.906) with
similar categories in LIWC.
