Human-in-the-loop topic modelling incorporates users' knowledge into the
modelling process, enabling them to refine the model iteratively. Recent
research has demonstrated the value of user feedback, but there are still
issues to consider, such as the difficulty in tracking changes, comparing
different models and the lack of evaluation based on real-world examples of
use. We developed a novel, interactive human-in-the-loop topic modeling system
with a user-friendly interface that enables users compare and record every step
they take, and a novel topic words suggestion feature to help users provide
feedback that is faithful to the ground truth. Our system also supports not
only what traditional topic models can do, i.e., learning the topics from the
whole corpus, but also targeted topic modelling, i.e., learning topics for
specific aspects of the corpus. In this article, we provide an overview of the
system and present the results of a series of user studies designed to assess
the value of the system in progressively more realistic applications of topic
modelling.

开发了一种新型交互式人机协同的主题建模系统，支持对语料库特定方面的主题建模，具有易于使用的界面、记录与比较功能以及主题词推荐功能，经过多次用户研究验证，该系统在主题建模中的应用具有显著价值。