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The Human Touch: How Non-Expert Users Perceive, Interpret, And Fix Topic Models, Tak Yeon Lee, Alison Smith, Kevin Seppi, Niklas Elmqvist, Jordan Boyd-Graber, Leah Findlater

Faculty Publications

Topic modeling is a common tool for understanding large bodies of text, but is typically provided as a “take it or leave it” proposition. Incorporating human knowledge in unsupervised learning is a promising approach to create high-quality topic models. Existing interactive systems and modeling algorithms support a wide range of refinement operations to express feedback. However, these systems’ interactions are primarily driven by algorithmic convenience, ignoring users who may lack expertise in topic modeling. To better understand how non-expert users understand, assess, and refine topics, we conducted two user studies—an in-person interview study and an online crowdsourced study. These studies …