Using a topic modeling algorithm to find relevant materials in a large corpus of textual items is not new; however, to date there has been little investigation into its usefulness to end-users. This article describes two methods we used to research this issue. In both methods, we used an instance of HathiTrust containing a snapshot of art, architecture and art history records from early 2010, that was populated with navigable terms generated using the topic modeling algorithm. In the first method, we created an unmoderated environment in which people navigated this instance on their own without supervision. In the second method, we talked to expert users as they navigated this same HathiTrust instance. Our unmoderated testing environment resulted in some conflicting results (use of topic facets was high, but satisfaction rating was somewhat low), while our one-on-one sessions with expert users give us reason to believe that topics and other subject terms (LCSH) are best used in conjunction with each other. This is a possibility we are interested in researching further.
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