Revealing the spatial co-occurrence patterns of multi-emotions from social media data

Abstract

Emotions play a critical role in understanding human behaviors and are direct indicators of residents' well-being and quality of life. Assessing spatial-emotional interactions is crucial for human-centered urban planning and public mental health. However, prior research has focused on the spatial analysis of every single emotion, ignoring the intricate interactions between multi-emotions and space. To address this gap, we propose a novel framework to reveal the spatial co-occurrence patterns of multi-emotions using massive social media data in Wuhan, China. Specifically, the BERT (bidirectional encoder representations from transformers) pre-trained model is utilized to classify each post into one of five basic emotions. Given the implementation of the K-means algorithm on these emotional results, the emotion-based similarities among different grids are investigated. The qualitative and quantitative results reveal six spatial co-occurrence patterns of conflicting or consistent emotions in urban space, namely, happiness-fear, happiness-anger, balanced emotion, happiness dominated, happiness-surprise, and happiness-sadness. In particular, the balanced emotion pattern is the most prevalent and tends to be spatially concentrated in the city center, while patterns of happiness-anger and happiness-sadness are mainly observed in the suburbs. Plus, results of the Multinomial Logit Model (MNLM) indicate that the spatial multi-emotions co-occurrence patterns are significantly correlated with land use characteristics based on points-of-interest (POIs) data. These findings provide an innovative perspective for understanding the complex interactions between emotions and space, with theoretical and practical implications for designing and maintaining an emotionally healthy city.