  Temporal data (such as news articles or Twitter feeds) often consists of a
mixture of long-lasting trends and popular but short-lasting topics of
interest. A truly successful topic modeling strategy should be able to detect
both types of topics and clearly locate them in time. In this paper, we first
show that nonnegative CANDECOMP/PARAFAC decomposition (NCPD) is able to
discover topics of variable persistence automatically. Then, we propose
sparseness-constrained NCPD (S-NCPD) and its online variant in order to
actively control the length of the learned topics effectively and efficiently.
Further, we propose quantitative ways to measure the topic length and
demonstrate the ability of S-NCPD (as well as its online variant) to discover
short and long-lasting temporal topics in a controlled manner in semi-synthetic
and real-world data including news headlines. We also demonstrate that the
online variant of S-NCPD reduces the reconstruction error more rapidly than
S-NCPD.
