Analogies are considered to be one of the core concepts of human cognition and communication, and are very efficient at encoding complex information in a natural fashion. However, computational approaches towards large-scale analysis of the semantics of analogies are hampered by the lack of suitable corpora with real-life example of analogies. In this paper we therefore propose a workflow for discriminating and extracting natural-language analogy statements from the Web, focusing on analogies between locations mined from travel reports, blogs, and the Social Web. For realizing this goal, we employ feature-rich supervised learning models which we extensively evaluate. We also showcase a crowd-supported workflow for building a suitable Gold dataset used for this purpose. The resulting system is able to successfully learn to identify analogies to a high degree of accuracy (F-Score 0.9) by using a high-dimensional subsequence feature space.