  The unprecedented outbreak of Severe Acute Respiratory Syndrome Coronavirus-2
(SARS-CoV-2), or COVID-19, continues to be a significant worldwide problem. As
a result, a surge of new COVID-19 related research has followed suit. The
growing number of publications requires document organization methods to
identify relevant information. In this paper, we expand upon our previous work
with clustering the CORD-19 dataset by applying multi-dimensional analysis
methods. Tensor factorization is a powerful unsupervised learning method
capable of discovering hidden patterns in a document corpus. We show that a
higher-order representation of the corpus allows for the simultaneous grouping
of similar articles, relevant journals, authors with similar research
interests, and topic keywords. These groupings are identified within and among
the latent components extracted via tensor decomposition. We further
demonstrate the application of this method with a publicly available
interactive visualization of the dataset.
