Measuring urban sentiments from social media data: a dual-polarity metric approach

Yong Gao (), Yuanyuan Chen, Lan Mu, Shize Gong, Pengcheng Zhang and Yu Liu
Additional contact information
Yong Gao: Peking University
Yuanyuan Chen: Peking University
Lan Mu: University of Georgia
Shize Gong: Peking University
Pengcheng Zhang: GuangZhou Urban Planning and Design Survey Research Institute
Yu Liu: Peking University

Journal of Geographical Systems, 2022, vol. 24, issue 2, No 4, 199-221

Abstract: Abstract Urban sentiment, as people’ perception of city environment and events, is a direct indicator of the quality of life of residents and the unique identity of a city. Social media by which people express opinions directly provides a way to measure urban sentiment. However, it is challenging to depict collective sentiments when integrating the posts inside a particular place, because the sentiment polarities will eventually be neutralized and consequently result in misinterpretation. It is necessary to capture positive and negative emotions distinguishingly rather than integrating them indiscriminately. Following the psychological hypothesis that two polar emotions are processed in parallel and can coexist independently, a novel dual-polarity metric is proposed in this paper to simultaneously evaluate collective positive and negative sentiments in geotagged social media in a place. This new measurement overcomes the integration problem in traditional methods, and therefore can better capture collective urban sentiments and diverse perceptions of places. In a case study of Beijing, China, urban sentiments are extracted using this approach from massive geotagged posts on Sina Weibo, a Twitter-like social media platform in China, and then their spatial distribution and temporal rhythm are revealed. Positive sentiments are more spatially heterogeneous than negative sentiments. Positive sentiments are concentrated in scenic spots, commercial and cultural areas, while negative sentiments are mostly around transportation hubs, hospitals and colleges. Following the principle of sense of place, multi-source data are integrated to evaluate the effects of influencing factors. The variation of spatial factors aggravates the heterogeneity of urban sentiment. The discovered spatiotemporal patterns give an insight into the urban sentiment through online behaviors and can help to improve city functionality and sustainability.

Keywords: Sentiment analysis; Spatiotemporal pattern; Social media; Place (search for similar items in EconPapers)
JEL-codes: C38 R23 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
http://link.springer.com/10.1007/s10109-021-00369-z Abstract (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:kap:jgeosy:v:24:y:2022:i:2:d:10.1007_s10109-021-00369-z

Ordering information: This journal article can be ordered from
http://www.springer. ... ce/journal/10109/PS2

DOI: 10.1007/s10109-021-00369-z

Access Statistics for this article

Journal of Geographical Systems is currently edited by Manfred M. Fischer and Antonio Páez

More articles in Journal of Geographical Systems from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().