In this paper we present a hotel filter recommendation method designed to address the cognitive load users face in an overchoice scenario. As online products and services are continuously diversifying, user needs are also becoming increasingly sophisticated. However, with more items to choose from, grasping the entire choice set and differentiating among all matching options becomes increasingly difficult, leading to sub-optimal outcomes. Conventional hotel reservation platforms provide with a limited set of additional filters, but these can not accommodate all intricate user needs. Employing natural language processing and machine learning techniques, we provide a simple framework that identifies meaningful filters from customer reviews. We define criteria and scoring methods to acquire relevant and interesting filters that may help customers refine their needs or even identify hidden, previously unknown ones. Our simulated user experiments show that our proposal is capable of identifying intricate and useful filters, leading to increased customer satisfaction.