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