Exploring the construction and application of “urban sentiment maps” in urban planning practice: A case study of Chengdu

Urban planning must be able to perceive and respond to the sentiments of city residents. Spatial-temporal data from social media comments contains a vast amount of subjective information, which can support condition analysis and the evaluation of planning proposals. This study takes Chengdu as a case study, employing natural language processing (NLP) techniques, such as sentiment analysis and topic extraction, to create “Urban Sentiment Maps” tailored to the practical needs of urban planning. These maps have been applied in different kinds of projects, enhancing the accuracy of condition analysis and the scientific validity of planning, ultimately improving post-evaluation of proposals.