Title: Topic supervised non-negative matrix factorization,
Abstract: Topic models have been extensively used to organize and interpret the
contents of large, unstructured corpora of text documents. Although topic
models often perform well on traditional training vs. test set evaluations, it
is often the case that the results of a topic model do not align with human
interpretation. This interpretability fallacy is largely due to the
unsupervised nature of topic models, which prohibits any user guidance on the
results of a model. In this paper, we introduce a semi-supervised method called
topic supervised non-negative matrix factorization (TS-NMF) that enables the
user to provide labeled example documents to promote the discovery of more
meaningful semantic structure of a corpus. In this way, the results of TS-NMF
better match the intuition and desired labeling of the user. The core of TS-NMF
relies on solving a non-convex optimization problem for which we derive an
iterative algorithm that is shown to be monotonic and convergent to a local
optimum. We demonstrate the practical utility of TS-NMF on the Reuters and
PubMed corpora, and find that TS-NMF is especially useful for conceptual or
broad topics, where topic key terms are not well understood. Although
identifying an optimal latent structure for the data is not a primary objective
of the proposed approach, we find that TS-NMF achieves higher weighted Jaccard
similarity scores than the contemporary methods, (unsupervised) NMF and latent
Dirichlet allocation, at supervision rates as low as 10% to 20%.