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PloS one. 2023 Apr 27;18(4):e0285175. doi: 10.1371/journal.pone.0285175 Q22.62024

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

基于情感知识增强的预训练和潜在狄利克雷分配的城市更新公众情绪分析大规模数据研究 翻译改进

Kehao Chen  1, Guiyu Wei  2

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作者单位

  • 1 School of Management Science and Real Estate, Chongqing University, Chongqing, China.
  • 2 School of Geography and Ecotourism, Southwest Forestry University, Yunnan, China.
  • DOI: 10.1371/journal.pone.0285175 PMID: 37104499

    摘要 Ai翻译

    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 2022; (3) Spatially, at the provincial level, Guangdong posted the most comments and Tibet, Shanghai, Guizhou, Chongqing, and Hong Kong are provinces with highly positive sentiment. At the national level, the east and south coastal, southwestern, and western China regions are more positive, as opposed to the northeast, central, and northwest regions; (4) Topics related to Shenzhen's renovations, development of China's urban regeneration and complaints from residents are validly categorized and become the public's key focus. Accordingly, governments should address spatiotemporal disparities and concerns of local residents for future development of urban regeneration.

    Keywords:public sentiment analysis; urban regeneration; massive data study

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    期刊名:Plos one

    缩写:PLOS ONE

    ISSN:1932-6203

    e-ISSN:1932-6203

    IF/分区:2.6/Q2

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