  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.
