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基于微博文本数据 (2019-2023) 分析广州市的图像感知
Huimin Qu1, Bor Tsong Teh1, Nikmatul Adha Nordin1
1Centre for Sustainable Urban Planning and Real Estate (SUPRE), Faculty of Built Environment, Universiti Malaya, 50603, Kuala Lumpur, Malaysia.
Heliyon
|September 12, 2024
概括
分析了从2019年到2023年的37万条微博帖子,发现广州.
科学领域:
- 城市研究 城市研究
- 社交媒体分析 社交媒体分析
- 计算语言学 计算语言学
背景情况:
- 社交媒体产生了大量的城市文本数据,为城市形象感知提供了新的途径.
- 在像新浪微博这样的平台上了解城市形象对于提高城市竞争力至关重要.
研究的目的:
- 从微博数据挖掘广州的城市图像特征.
- 为制定广州形象沟通战略提供数据驱动的见解.
- 在城市形象管理中为其他城市提供参考.
主要方法:
- 网络爬虫技术用于数据收集 (37万条微博消息,2019-2023年).
- 文本分析技术:术语频率-反向文档频率 (TF-IDF),隐性迪里克莱特分配 (LDA) 和情感分析 (SnowNLP).
主要成果:
- 文本挖掘有效地捕捉到城市动态和趋势.
- 确定了广州城市形象的特定特征.
- 展示了社交媒体数据对改进城市形象感知的实用性.
结论:
- 对社交媒体的广泛数据分析为城市形象提供了及时的见解.
- 调查结果支持有针对性的沟通策略,以提高广州的形象.
- 这种方法为全球城市形象管理提供了有价值的参考.
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