Lexicons on Demand: Neural Word Embeddings for Large-Scale Text Analysis.

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 learning a neural embedding across billions of words on the web. 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 such as 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.