Public emotions and visual perception of the East Coast Park in Singapore: A deep learning method using social media data

Abstract

Social media data mining has become a prevailing approach to understanding people’s emotional response to, and perception of, public spaces. Drawing on Google Maps Reviews’ text reviews and shared images, this study develops a deep learning framework that combines Transformer BERT and CNN-VGG models to explore public emotions and visual perception of the East Coast Park (ECP) in Singapore. The results show that (1) public emotions of the ECP are predominantly joy, with minor emotional fluctuations in 2020 due to the pandemic lockdown; (2) CNN-VGG image classification is an effective tool to capture people’s visual preferences of public spaces, (3) public emotions across 12 specific public spaces are predominantly joy and neutral, but the results of people’s visual preference exhibited considerable diversity, and (4) image data is mostly associated with joy or neutral emotions, generally indicating users’ satisfaction of ECP public space, and for this reason, there is a lack of sound base to identify environmental factors associated with people’s negative emotions. This study shows that the proposed deep learning framework is effective in using social media data to understand the public's emotional response to and visual preference of urban public spaces.