  To address the lack of comparative evaluation of Human-in-the-Loop Topic
Modeling (HLTM) systems, we implement and evaluate three contrasting HLTM
modeling approaches using simulation experiments. These approaches extend
previously proposed frameworks, including constraints and informed prior-based
methods. Users should have a sense of control in HLTM systems, so we propose a
control metric to measure whether refinement operations' results match users'
expectations. Informed prior-based methods provide better control than
constraints, but constraints yield higher quality topics.
