The automatic extraction of urban perception shared by people on location-based social networks (LBSNs) is an important multidisciplinary research goal. One of the reasons is because it facilitates the understanding of the intrinsic characteristics of urban areas in a scalable way, helping to leverage new services. However, content shared on LBSNs is diverse, encompassing several topics, such as politics, sports, culture, religion, and urban perceptions, making the task of content extraction regarding a particular topic very challenging. Considering free-text messages shared on LBSNs, we propose an automatic and generic approach to extract people's perceptions. For that, our approach explores opinions that are spatial-temporal and semantically similar. We exemplify our approach in the context of urban outdoor areas in Chicago, New York City and London. Studying those areas, we found evidence that LBSN data brings valuable information about urban regions. To analyze and validate our outcomes, we conducted a temporal analysis to measure the results' robustness over time. We show that our approach can be helpful to better understand urban areas considering different perspectives. We also conducted a comparative analysis based on a public dataset, which contains volunteers' perceptions regarding urban areas expressed in a controlled experiment. We observe that both results yield a very similar level of agreement.