Public sentiment analysis on urban regeneration: A massive data study based on sentiment knowledge enhanced...

Area of Science:

• Urban Studies
• Social Science
• Data Science

Background:

• Public satisfaction is crucial for China's urban regeneration initiatives.
• This research pioneers the use of big data for sentiment analysis of public opinions on urban regeneration in China.

Purpose of the Study:

• To analyze public sentiment regarding China's urban regeneration using massive datasets.
• To identify spatiotemporal patterns and key themes in public comments on urban regeneration.

Main Methods:

• Utilized Natural Language Processing (NLP), Knowledge Enhanced Pre-Training, Word Cloud, and Latent Dirichlet Allocation (LDA).
• Analyzed public comments sourced from social media, online forums, and government platforms.

Main Results:

• Overall public sentiment towards urban regeneration is positive, with notable spatiotemporal variations.
• Sentiment peaked positively in 2021, was most negative in 2020, and became consistently negative in 2022.
• Geographically, eastern and southern coastal, southwestern, and western China show more positive sentiment compared to other regions. Guangdong had the most comments, with Tibet, Shanghai, Guizhou, Chongqing, and Hong Kong showing highly positive sentiment.
• Key public concerns include Shenzhen's renovations, regeneration development, and resident complaints.

Conclusions:

• Governments must address spatiotemporal disparities in public sentiment for effective urban regeneration.
• Understanding and responding to local resident concerns is vital for future urban regeneration strategies.