Lexical and Hierarchical Topic Regression

Part of Advances in Neural Information Processing Systems 26 (NIPS 2013)

Bibtex »Metadata »Paper »Reviews »

Authors

Viet-An Nguyen, Jordan L. Ying, Philip Resnik

Abstract

<p>Inspired by a two-level theory that unifies agenda setting and ideological framing, we propose supervised hierarchical latent Dirichlet allocation (SHLDA) which jointly captures documents' multi-level topic structure and their polar response variables. Our model extends the nested Chinese restaurant process to discover a tree-structured topic hierarchy and uses both per-topic hierarchical and per-word lexical regression parameters to model the response variables. Experiments in a political domain and on sentiment analysis tasks show that SHLDA improves predictive accuracy while adding a new dimension of insight into how topics under discussion are framed.</p>