Public sentiment analysis on urban regeneration: A massive data study based on sentiment knowledge enhanced pre-training and latent Dirichlet allocation.

ABSTRACT:

Background

Public satisfaction is the ultimate goal and an important determinant of China's urban regeneration plan. This study is the first to use massive data to perform sentiment analysis of public comments on China's urban regeneration.

Methods

Public comments from social media, online forums, and government affairs platforms are analyzed by a combination of Natural Language Processing, Knowledge Enhanced Pre-Training, Word Cloud, and Latent Dirichlet Allocation.

Results

(1) Public sentiment tendency toward China's urban regeneration was generally positive but spatiotemporal divergences were observed; (2) Temporally, public sentiment was most negative in 2020, but most positive in 2021. It has remained consistently negative in 2022, particularly after February 202

SUBMITTER: Chen K 

PROVIDER: S-EPMC10138235 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature