  Changepoint analysis deals with unsupervised detection and/or estimation of
time-points in time-series data, when the distribution generating the data
changes. In this article, we consider \emph{offline} changepoint detection in
the context of large scale textual data. We build a specialised temporal topic
model with provisions for changepoints in the distribution of topic
proportions. As full likelihood based inference in this model is
computationally intractable, we develop a computationally tractable approximate
inference procedure. More specifically, we use sample splitting to estimate
topic polytopes first and then apply a likelihood ratio statistic together with
a modified version of the wild binary segmentation algorithm of Fryzlewicz et
al. (2014). Our methodology facilitates automated detection of structural
changes in large corpora without the need of manual processing by domain
experts. As changepoints under our model correspond to changes in topic
structure, the estimated changepoints are often highly interpretable as marking
the surge or decline in popularity of a fashionable topic. We apply our
procedure on two large datasets: (i) a corpus of English literature from the
period 1800-1922 (Underwoodet al., 2015); (ii) abstracts from the High Energy
Physics arXiv repository (Clementet al., 2019). We obtain some historically
well-known changepoints and discover some new ones.
