Empath: Understanding Topic Signals in Large Scale Text
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.
CHI