  Over the years, topic models have provided an efficient way of extracting
insights from text. However, while many models have been proposed, none are
able to model topic temporality and hierarchy jointly. Modelling time provide
more precise topics by separating lexically close but temporally distinct
topics while modelling hierarchy provides a more detailed view of the content
of a document corpus. In this study, we therefore propose a novel method,
HTMOT, to perform Hierarchical Topic Modelling Over Time. We train HTMOT using
a new implementation of Gibbs sampling, which is more efficient. Specifically,
we show that only applying time modelling to deep sub-topics provides a way to
extract specific stories or events while high level topics extract larger
themes in the corpus. Our results show that our training procedure is fast and
can extract accurate high-level topics and temporally precise sub-topics. We
measured our model's performance using the Word Intrusion task and outlined
some limitations of this evaluation method, especially for hierarchical models.
As a case study, we focused on the various developments in the space industry
in 2020.
