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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.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Design\/methodology\/approach<\/jats:title><jats:p>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.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Findings<\/jats:title><jats:p>Results reveal gaps between the public\u2019s impression and the strategic goals of the government in traffic and environment.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Originality\/value<\/jats:title><jats:p>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.<\/jats:p><\/jats:sec>","DOI":"10.1108\/el-03-2022-0064","type":"journal-article","created":{"date-parts":[[2022,7,13]],"date-time":"2022-07-13T06:59:12Z","timestamp":1657695552000},"page":"413-434","source":"Crossref","is-referenced-by-count":6,"title":["Dynamic sentiment sensing of cities with social media 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