  Nonnegative matrix factorization can be used to automatically detect topics
within a corpus in an unsupervised fashion. The technique amounts to an
approximation of a nonnegative matrix as the product of two nonnegative
matrices of lower rank. In this paper, we show this factorization can be
combined with regression on a continuous response variable. In practice, the
method performs better than regression done after topics are identified and
retrains interpretability.
