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Abstract:
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.
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https://dl.acm.org/doi/10.1145/2858036.2858535
https://arxiv.org/pdf/1602.06979.pdf
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