Noise is one of the most important factors that worsen the quality of life in cities. Beyond the physiological effects of noise, there is a significant subjective component, which is reflected as a community response that has been traditionally measured through costly and commonly biased surveys. With the boost of Online Social Networks, people have begun to share their opinions and feelings about everyday problems, including noise. We believe that this data can be a trustworthy source of information and its analysis could provide the citizens’ perspective regarding several aspects about noise or about specific sound sources. It could also provide insights on the performance of actions against noise, which would become essential to improve policymaking in the future. The goal of my research is to validate this idea, and to develop a system that gathers, preprocess and analyzes text data from Online Social Networks with this aim, using technologies such as Natural Language Processing, aspect-based sentiment analysis and Artificial Intelligence.