  Topic trajectory information provides crucial insight into the dynamics of
topics and their evolutionary relationships over a given time. Also, this
information can help to improve our understanding on how new topics have
emerged or formed through a sequential or interrelated events of emergence,
modification and integration of prior topics. Nevertheless, the implementation
of the existing methods for topic trajectory identification is rarely available
as usable software. In this paper, we present TopicTracker, a platform for
topic trajectory identification and visualisation. The key of Topic Tracker is
that it can represent the three facets of information together, given two kinds
of input: a time-stamped topic profile consisting of the set of the underlying
topics over time, and the evolution strength matrix among them: evolutionary
pathways of dynamic topics, evolution states of the topics, and topic
importance. TopicTracker is a publicly available software implemented using the
R software.
