2016/02/22 by Ethan Fast, Binbin Chen, Michael Bernstein · 2 citations
Computer Science · #cs.CL #cs.AI
paper · pdf · doi:10.1145/2858036.2858535
CHI: ACM Conference on Human Factors in Computing Systems 2016
arxiv created 2016/02/22 · arxiv updated 2016/02/24
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.