Is Your Anchor Going Up or Down? Fast and Accurate Supervised Topic Models

Thang Nguyen, Jordan L. Boyd-Graber, Jeffrey Lund, Kevin D. Seppi, Eric K. Ringger

Abstract: Topic models provide insights into document collections, and their supervised extensions also capture associated document-level metadata such as sentiment. However, inferring such models from data is often slow and cannot scale to big data. We build upon the “anchor” method for learning topic models to capture the relationship between metadata and latent topics by extending the vector-space representation of word-cooccurrence to include metadataspecific dimensions. These additional dimensions reveal new anchor words that reflect specific combinations of metadata and topic. We show that these new latent representations predict sentiment as accurately as supervised topic models, and we find these representations more quickly without sacrificing interpretability.