Urban landscape and climate affect residents' sentiments based on big data

Residents' sentiments are a quantitative indicator of human feelings, which is useful for optimizing urban res-idential environments. Little is known about the spatiotemporal variations and potential drivers of sentiment based on big data. A total of 221,104 Weibo social media data were used to quantify daily sentiment in the Beijing metropolitan area during the COVID-19 pandemic from January 1, 2021 to March 8, 2022. Deep-learning natural language processing was used to extract this dataset to investigate the spatiotemporal sentiment patterns. The density of roads and buildings, normalized difference vegetation index (NDVI), population, sky visibility factor, daily land surface temperature (LST), daily precipitation, and daily air pollution concentrations (CO, NO2, PM2.5, SO2, and O3) were explored as potential drivers of sentiment. Results show that (1) the holiday sentiment was 1.31% higher than on weekends and 4.61% higher than on weekdays. Extreme precipitation, air pollution, and COVID-19 lockdown measures have reduced sentiment. (2) The sentiment in spring was found to be the highest. The numbers of functional zones with high sentiment values (>0.8) in spring were 13.59%, in summer 34.48%, and in autumn 14.71%. (3) Sentiment was highest under conditions of moderate greenness (0.4<0.6) and comfortable daily temperature (25 degrees C < LST<30 degrees C). (4) Sentiment was negatively asso-ciated with daily air pollutants, such as PM2.5, NO2, and CO. This paper presents the effectiveness of sentiment quantification based on social media data and deep-learning techniques. The results provide practical implica-tions and support decisions for sustainable urban health development.

Automated summary

This study quantifies the sentiment fluctuations in the Beijing metropolitan area based on Weibo social media data and investigates their spatiotemporal variations and potential drivers. The results show that sentiment is higher during holidays, while extreme precipitation, air pollution, and COVID-19 lockdown measures reduce sentiment. Spring has the highest sentiment, and moderate greenness and comfortable daily temperature also contribute to higher sentiment. Daily air pollutants such as PM2.5, NO2, and CO are negatively associated with sentiment.