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The paucity of rigorous evaluation measures undermines topic modeling results' validity and trustworthiness. Accordingly, we propose a method that researchers can use to select models when they assess topics' human interpretability. We show how they can evaluate different topic models using gold-standard sets that humans label. Our approach ensures that the topics extracted algorithmically from an entire corpus concur with the themes humans would have identified in the same documents. By doingdoi:10.17705/1cais.04720 fatcat:2ozfytv23fczrmei6xdr2rmnm4