Urban Green Spaces are essential components of urban planning, fostering social interaction, improving public health, and promoting sustainability. Within the framework of a new model for more livable, healthy, and sustainable cities, the revitalization of these areas is becoming increasingly important. In this context, citizen perception serves as a valuable source of information for understanding how users value these spaces. This work analyzes user comments published on a digital platform about one of Madrid’s most iconic urban parks, the Casa de Campo Park. It aims to identify the most relevant thematic dimensions in these opinions and to classify each review into a single category based on its predominant content. To this end, an analytical approach is proposed that combines qualitative and computational techniques, using predictive classification models suited to the nature of the data. The functionality of this proposal is demonstrated using a dataset we collected from Google Maps reviews of Casa de Campo Park. The results show that this approach structures dispersed opinions into comparable data and enhances the online search experience by organizing reviews based on users’ interests. It offers a replicable method for other urban settings, helping improve public spaces and align them with social needs.