Memory, Morphology, and Meaning: An NLP-Based Analysis of Public Perceptions of Milan’s Postwar ERP Estates

Between 1940 and 1980, large scale social housing construction in Milan reshaped the city’s modern urban identity, yet how these postwar estates are perceived today remains insufficiently documented. This study analyzes user-generated Google Maps reviews to examine how these neighborhoods are evaluated in terms of sentiment and recurring narratives. We develop a replicable Natural Language Processing (NLP) workflow that combines automated translation, sentiment scoring, thematic keyword extraction, and unsupervised clustering to compare perception patterns across construction decades and architectural typologies, and we synthesize multi dimensional signals into a Perceived Space Quality Index (PSQI). Results show clear differentiation by typology and period: earlier courtyard-based estates are more frequently associated with positive evaluations, while later open block modernist developments exhibit more polarized discourse. Across clusters, positive evaluations most often co-occur with references to well-maintained shared spaces and active everyday life, while negative discourse concentrates on neglect and insecurity. Review narratives also increasingly foreground social experience and reputation over formal description. Overall, the workflow supports comparative reading of public narratives across estates and highlights which themes most shape favorable or unfavorable perception.