Exploring spatiotemporal changes in the multi-granularity emotions of people in the city: a case study of Nanchang, China

Xin Xiao,Chaoyang Fang,Li Liu,Ya Tian,Qinghua He,Hui Lin

doi:10.1007/s43762-021-00030-x

Abstract

In the Internet age, emotions exist in cyberspace and geospatial space, and social media is the mapping from geospatial space to cyberspace. However, most previous studies pay less attention to the multidimensional and spatiotemporal characteristics of emotion. We obtained 211,526 Sina Weibo data with geographic locations and trained an emotion classification model by combining the Bidirectional Encoder Representation from Transformers (BERT) model and a convolutional neural network to calculate the emotional tendency of each Weibo. Then, the topic of the hot spots in Nanchang City was detected through a word shift graph, and the temporal and spatial change characteristics of the Weibo emotions were analyzed at the grid-scale. The results of our research show that Weibo’s overall emotion tendencies are mainly positive. The spatial distribution of the urban emotions is extremely uneven, and the hot spots of a single emotion are mainly distributed around the city. In general, the intensity of the temporal and spatial changes in emotions in the cities is relatively high. Specifically, from day to night, the city exhibits a pattern of high in the east and low in the west. From working days to weekends, the model exhibits a low center and a four-week high. These results reveal the temporal and spatial distribution characteristics of the Weibo emotions in the city and provide auxiliary support for analyzing the happiness of residents in the city and guiding urban management and planning.

Highlights

The city is not a cold concrete space, and it carries people’s rich emotional experience
5.1 The spatiotemporal distribution of emotions is heterogeneous In this study, a Bidirectional Encoder Representation from Transformers (BERT)-CNN emotion classification model was trained, which combines BERT based on pre-training with a convolutional neural network to give full play to their respective advantages
6 Conclusions In this study, an efficient multi granularity emotion classification model was constructed using pre-trained Bert and a convolution neural network, the emotional tendency of 211,526 Sina Weibo data with geographical locations in Nanchang was calculated, the spatial distribution characteristics of urban Weibo emotions were determined through emotional hotspot analysis, and the emotional topics were analyzed using word shift graphs

Summary

Introduction

The city is not a cold concrete space, and it carries people’s rich emotional experience. The differences in the types and intensity of emotions in different parts of the city and in different public groups can reflect the macroscopic social production and the spatial distribution of the social classes in the city. In such diverse urban events as politics (Matalon et al, 2021), economy (Wan et al, 2021), flu viruses (Alamoodi et al, 2021), and natural disasters Using machine learning related techniques to mine and understand the content characteristics of social media, analyze the emotions of social media users, and understand the topics on social media in different areas of the city has attracted the interest of many researchers

Methods

Results

Discussion

Conclusion