This chapter introduces a framework for analyzing social media data for municipal decision support. It combines methods from fuzzy set theory, sentiment analysis, and topic modeling—three fields introduced in the previous two chapters. In short, the framework first determines the sentiment of each individual social media post, identifies most discussed topics across all social media posts at the input, and assigns membership degreed of each social media post to each topic. These pieces of information are then combined to create a triangular fuzzy number describing the overall sentiment toward a given topic. This TFN can then be optionally transformed into a simplified representation: levels of positive and negative sentiment. Besides introducing the overall design of the framework, the selection of specific sentiment analysis and topic modeling methods is also justified, and the construction of triangular fuzzy numbers for individual topics, as well as the calculation of the levels of positive and negative sentiments in depth, is described.