A great deal of research on information extraction from textual datasets has been performed in specific data contexts, such as movie reviews, commercial product evaluations, campaign speeches, etc. In this paper, we raise the question on how appropriate these methods are for documents related to land-use planning. The kind of information sought concerns the stakeholders, sentiments, geographic information, and everything else related to the territory. However, it is extremely challenging to link sentiments to the three dimensions that constitute geographic information (location, time, and theme). After highlighting the limitations of existing proposals and discussing issues related to textual data, we present a method called OPILAND (OPinion mIning from LAND-use planning documents) designed to semi-automatically mine opinions related to named-entities in specialized contexts. Experiments are conducted on a Thau lagoon dataset (France), and then applied on three datasets that are related to different areas in order to highlight the relevance and the broader applications of our proposal.