Urban sentiment mapping using language and vision models in spatial analysis

Abstract

Introduction: Understanding how urban environments shape public sentiment is crucial for urban planning. Traditional methods, such as surveys, often fail to capture evolving sentiment dynamics. This study leverages language and vision models to assess the influence of urban features on public emotions across spatial contexts and timeframes. Methods: A two-phase computational framework was developed. First, sentiment inference used a BERT-based model to extract sentiment from geotagged social media posts. Second, urban context inference applied PSPNet and Mask R-CNN to street view imagery to quantify urban design features, including visual enclosure, human scale, and streetscape complexity. The study integrates publicly available data and spatial simulation techniques to examine sentiment-urban form relationships over time. Results: The analysis reveals that greenery and pedestrian-friendly infrastructure positively influence sentiment, while excessive openness and fenced-off areas correlate with negative sentiment. A hotspot analysis highlights shifting sentiment patterns, particularly during societal disruptions like the COVID-19 pandemic. Discussion: Findings emphasize the need to incorporate public sentiment into urban simulations to create inclusive, safe, and resilient environments. The study provides data-driven insights for planners, supporting human-centered design interventions that enhance urban livability.