Using Social Media Data to Explore Urban Land Value and Sentiment Inequality: A Case Study of Xiamen, China

Differences in urban land values affect residents’ living experiences and may contribute to sentiment inequality. Due the popularity of smart mobile devices social media platforms, online tweets with location information can be used as objective reflect differences residents different locations, overcoming limitations previous studies small sample sizes or a lack spatial information. Sentiment quantification based on deep learning enables identification patterns sentiments. It also provides new approach for analyzing data from big platforms using an intelligent computing platform. This paper quantitatively analyzes contained analysis algorithm reveal inequalities between sentiments values. The Baidu Intelligent Cloud platform is identify 460,000 Weibo Xiamen, China, 2020. We analyze positive negative create distribution map. concentration curve indicates inequality impact high index (CI) correlation show that CI value 0.07, significant exists due value. use tweet reference future interdisciplinary research psychology, planning, geography, sociology. proposed insights into multiplatform interaction context Internet Everything.