Journal of Geodesy and Geoinformation Science ›› 2022, Vol. 5 ›› Issue (2): 49-59.doi: 10.11947/j.JGGS.2022.0206

• Special Issue • Previous Articles     Next Articles

Understanding Citizens’emotion States under the Urban Livability Environment through Social Media Data: a Case Study of Wuhan

Lai CHEN(),Chaogui KANG,Chao YANG()   

  1. National Engineering Research Center for Geographic Information System, China University of Geosciences (Wuhan), Wuhan 430074, China
  • Received:2021-11-01 Accepted:2022-04-27 Online:2022-06-20 Published:2022-07-22
  • Contact: Chao YANG;
  • About author:Lai CHEN, E-mail:
  • Supported by:
    National Key Research and Development Program of China(2020YFB2103402)


It is recognized that a city with a livable environment can bring happiness to residents. In this study, we explored the social media users’ emotional states in their current living spaces and found out the relationship between the social media users’ emotions and urban livability. We adopt six urban livability indicators (including education, medical services, public facilities, leisure places, employment, and transportation) to construct city livable indices. Also, the Analytic Hierarchy Process (AHP) spatial statistic method is applied to identify and analyze the different habitable regions of Wuhan City. In terms of citizen’s emotion analysis, we use Long Short-Term Memory (LSTM) neural network to analyze the Weibo text and obtain the Weibo users’sentiment scores. The correlation analysis of residents’ emotions and city livability results shows a positive correlation between the livable city areas (i.e., the area with higher livable ranking indices) and Weibo users’ sentiment scores (with a Pearson correlation coefficient of 0.881 and P-Value of 0.004). In other words, people who post Weibo in high livability areas of Wuhan express more positive emotional states. Still, emotion distribution varies in different regions, which is mainly caused by people’s distribution and the diversity of the city’s functional areas.

Key words: urban livability; sentiment analysis; Sina Weibo; Analytic Hierarchy Process; natural language processing; Long Short-Term Memory