  Fully unsupervised topic models have found fantastic success in document
clustering and classification. However, these models often suffer from the
tendency to learn less-than-meaningful or even redundant topics when the data
is biased towards a set of features. For this reason, we propose an approach
based upon the nonnegative matrix factorization (NMF) model, deemed
\textit{Guided NMF}, that incorporates user-designed seed word supervision. Our
experimental results demonstrate the promise of this model and illustrate that
it is competitive with other methods of this ilk with only very little
supervision information.
