SAP-sLDA: An Interpretable Interface for Exploring Unstructured Text
Charumathi Badrinath Weiwei Pan Finale Doshi-Velez
A common way to explore text corpora is through low-dimensional projections
of the documents, where one hopes that thematically similar documents will be
clustered together in the projected space. However, popular algorithms for
dimensionality reduction of text corpora, like Latent Dirichlet Allocation
(LDA), often produce projections that do not capture human notions of document
similarity. We propose a semi-supervised human-in-the-loop LDA-based method for
learning topics that preserve semantically meaningful relationships between
documents in low-dimensional projections. On synthetic corpora, our method
yields more interpretable projections than baseline methods with only a
fraction of labels provided. On a real corpus, we obtain qualitatively similar
results.