  There is currently an unprecedented demand for large-scale temporal data
analysis due to the explosive growth of data. Dynamic topic modeling has been
widely used in social and data sciences with the goal of learning latent topics
that emerge, evolve, and fade over time. Previous work on dynamic topic
modeling primarily employ the method of nonnegative matrix factorization (NMF),
where slices of the data tensor are each factorized into the product of
lower-dimensional nonnegative matrices. With this approach, however,
information contained in the temporal dimension of the data is often neglected
or underutilized. To overcome this issue, we propose instead adopting the
method of nonnegative CANDECOMP/PARAPAC (CP) tensor decomposition (NNCPD),
where the data tensor is directly decomposed into a minimal sum of outer
products of nonnegative vectors, thereby preserving the temporal information.
The viability of NNCPD is demonstrated through application to both synthetic
and real data, where significantly improved results are obtained compared to
those of typical NMF-based methods. The advantages of NNCPD over such
approaches are studied and discussed. To the best of our knowledge, this is the
first time that NNCPD has been utilized for the purpose of dynamic topic
modeling, and our findings will be transformative for both applications and
further developments.
