We apply the notion of popularity in machine-generated sentence evaluation to the queries used to search for documents. Our intuition is that queries composed of popular terms obtain more relevant documents and increase the probability that these documents contain the desired results. We measure the popularity of a query by analyzing a massive online document repository, Korean Wikipedia. To verify the influence of query popularity on search results, we conduct experiments to measure the mean reciprocal rank and precision-at-k, and perform an individual comparison of search term pairs. Through these experiments, we demonstrate that better search results can be obtained by considering the popularity of the query.