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作者

Liya Yang;Carlos Marmolejo Duarte;Pablo Martí Ciriquián

来源

CITIES,Vol.130,Issue1,Article 103977

语言

英文

关键字

Public sentiment;Twitter;Urban environment;Sentiment analysis

作者单位

Guangdong Guodi Planning Science Technology Co., Ltd., Guangzhou, 510650, China;Department of Architectural Technology, Polytechnic University of Catalonia, Av. Diagonal, 649, 08028 Barcelona, Spain;Department of Building Sciences and Urbanism, University of Alicante, Carretera San Vicente del Raspeig s/n, 03690 San Vicente del Raspeig, Alicante, Spain;Guangdong Guodi Planning Science Technology Co., Ltd., Guangzhou, 510650, China;Department of Architectural Technology, Polytechnic University of Catalonia, Av. Diagonal, 649, 08028 Barcelona, Spain;Department of Building Sciences and Urbanism, University of Alicante, Carretera San Vicente del Raspeig s/n, 03690 San Vicente del Raspeig, Alicante, Spain

摘要

Public sentiment provides an important social reference for urban management and planning. The relationship between public sentiment and a single type of land use has yielded stable results in previous studies. Hitherto, there has been relatively little research on the correlation of the entire urban environment with public sentiment. Based on the unit of statistical area in Barcelona city, this research uses Twitter sentiment to represent public sentiment and develops a regression model for understanding the interrelationship of four layers: sociodemographic, built-environment, human mobility and socioeconomic activities. The result shows that: 1) The long-term spatial difference in public sentiment has correlations with the urban environment, though it is not decisive. 2) Regardless of disruptive events that are directly associated with public sentiments, the wealthier areas show a more positive correlation with higher public sentiment. 3) The distribution of sentiment tweets (non-neutral) has a close relationship with places where there is a high flow of human activities. This study contributes to the systematic literature of urban applications of sentiment analysis with new empirical observations and a transferable methodology.