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Dynamic sentiment sensing of cities with social media data

Guanghui Ye (School of Information Management, Central China Normal University, Wuhan, China)
Ze Peng (School of Information Management, Central China Normal University, Wuhan, China)
Jinyu Wei (School of Information Management, Central China Normal University, Wuhan, China)
Lingzi Hong (College of Information, University of North Texas, Denton, Texas, USA)
SongYe Li (School of Information Management, Central China Normal University, Wuhan, China)
Chuan Wu (School of Information Management, Central China Normal University, Wuhan, China)

The Electronic Library

ISSN: 0264-0473

Article publication date: 14 July 2022

Issue publication date: 8 August 2022

249

Abstract

Purpose

A lot of people share their living or travelling experiences about cities by writing posts on social media. Such posts carry multi-dimensional information about the characteristics of cities from the public’s perspective. This paper aims at applying text mining technology to automatically extract city images, which are known as how observers perceive the status of the city, from these social media texts.

Design/methodology/approach

This paper proposes a data processing pipeline for automatic city image extraction and applies sentiment analysis, timing analysis and contrastive analysis in a case study on Wuhan, a central China megacity. Specifically, the city image constructed with social media text and the expected policy outcomes by the government are compared.

Findings

Results reveal gaps between the public’s impression and the strategic goals of the government in traffic and environment.

Originality/value

This study contributes a novel approach to assess government performance by complementary data from social media. This case study implies the value of social media-based city image in the identification of gaps for the optimization of government performance.

Keywords

Acknowledgements

This work is supported by National Natural Science Foundation of China (No.71804055) and National Social Science Foundation of China (No. 19ZDA345).

Citation

Ye, G., Peng, Z., Wei, J., Hong, L., Li, S. and Wu, C. (2022), "Dynamic sentiment sensing of cities with social media data", The Electronic Library, Vol. 40 No. 4, pp. 413-434. https://doi.org/10.1108/EL-03-2022-0064

Publisher

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Emerald Publishing Limited

Copyright © 2022, Emerald Publishing Limited

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