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 SAP-sLDA, a semi-supervised human-in-the-loop method for learning topics that preserve semantically meaningful relationships between documents in low-dimensional projections. On synthetic corpora, SAP-sLDA yields more interpretable projections than baseline methods with only a fraction of labels provided. On a real corpus, we obtain qualitatively similar results.