  Topic modeling, a method for extracting the underlying themes from a
collection of documents, is an increasingly important component of the design
of intelligent systems enabling the sense-making of highly dynamic and diverse
streams of text data. Traditional methods such as Dynamic Topic Modeling (DTM)
do not lend themselves well to direct parallelization because of dependencies
from one time step to another. In this paper, we introduce and empirically
analyze Clustered Latent Dirichlet Allocation (CLDA), a method for extracting
dynamic latent topics from a collection of documents. Our approach is based on
data decomposition in which the data is partitioned into segments, followed by
topic modeling on the individual segments. The resulting local models are then
combined into a global solution using clustering. The decomposition and
resulting parallelization leads to very fast runtime even on very large
datasets. Our approach furthermore provides insight into how the composition of
topics changes over time and can also be applied using other data partitioning
strategies over any discrete features of the data, such as geographic features
or classes of users. In this paper CLDA is applied successfully to seventeen
years of NIPS conference papers (2,484 documents and 3,280,697 words),
seventeen years of computer science journal abstracts (533,560 documents and
32,551,540 words), and to forty years of the PubMed corpus (4,025,978 documents
and 273,853,980 words).
