  Understanding how urban density impacts public perceptions of urban service
is important for informing livable, accessible, and equitable urban planning.
Conventional methods such as surveys are limited by their sampling scope, time
efficiency, and expense. On the other hand, crowdsourcing through online
platforms presents an opportunity for decision-makers to tap into a
user-generated source of information that is widely available and
cost-effective. To demonstrate such potential, we collect Google Maps reviews
for 23,906 points of interest (POIs) in Atlanta, Georgia. Next, we use the
Bidirectional Encoder Representations from Transformers (BERT) model to
classify reviewers' attitudes toward urban density and the Robustly Optimized
BERT approach (RoBERTa) to compute sentiment. Finally, a partial least squares
regression is fitted to examine the relationships between average sentiment and
socio-spatial factors. The findings reveal areas in Atlanta with predominantly
negative sentiments toward urban density and highlight the variation in
sentiment distribution across different POIs. Further, the regression analysis
reveals that minority and low-income communities often express more negative
sentiments, and higher land use density exacerbates such negativity. This study
introduces a novel data source and methodological framework that can be easily
adapted to different regions, offering useful insights into public sentiment
toward the built environment and shedding light on how planning policies can be
designed to handle related challenges.
