Exploring Social Interactions in Vancouver's Neighbourhood Parks Through Sentiment Analysis of Google Reviews

This fine-tuning process adapted the model’s attention toward content associated with social cohesion themes such as “welcoming,” “family,” “crowded,” and “unsafe.” The average park sentiment score was then used as the independent variable in a generalized linear regression (GLR) model, followed by geographically weighted regression (GWR) to account for spatial variation. Twenty candidate socioeconomic variables from the 2021 Canadian census were tested as predictors of average park sentiment.

Results showed that household size and median income were statistically significant positive predictors of higher park sentiment at the dissemination area (DA) scale. The findings highlight disparities in perceived social cohesion across Vancouver’s neighborhoods and reveal the social atmosphere of parks beyond Google star ratings alone, offering evidence-based insights for park planning and management. Key Words: Social cohesion, sentiment analysis, transfer learning, deep learning, geographically weighted regression, natural language processing