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.

翻译：人在回路中的主题建模将用户知识融入建模过程，使用户能够迭代优化模型。近期研究已证实用户反馈的价值，但仍存在若干待解决问题，包括追踪模型变更困难、不同模型间对比不便，以及缺乏基于真实使用案例的评估。我们开发了一种新型交互式人在回路主题建模系统，其用户友好界面支持用户记录并对比每一步操作，并创新性地引入主题词建议功能，帮助用户提供忠实于真实标注的反馈。该系统不仅支持传统主题建模的全部功能（即从整个语料库中学习主题），还支持定向主题建模（即针对语料库特定方面进行主题学习）。本文概述了该系统，并呈现一系列用户研究的结果，这些研究旨在评估该系统在逐步贴近真实应用场景下的实用价值。