Social networks have become an important channel for exchanging information and communication among citizens. Text mining, crowdsourcing and data visualization are some approaches that allow the information and knowledge extraction from texts in comment formats, exchanged between citizens in social networks. This movement can be indirectly used as a bias for popular participation, gaining prominence in the construction of smart cities. The objective of this work is to present a method that geocodes citizens’ comments made on posts in Social Network Pages of Local Government Agencies, and extracts the most frequent topics present in these comments. In order to validate our method, we implemented a web system that implements the steps of the proposed method, and conducted a case study. The tool, and consequently the steps of the presented method, was evaluated by four software developers, which indicated that the tool was easy to use, new knowledge could be extracted from it, and some interesting improvements were pointed out by them.