Qu Huimin, Teh Bor Tsong, Nordin Nikmatul Adha, Liang Zhuqin
Centre for Sustainable Urban Planning and Real Estate (SUPRE), Faculty of Built Environment, Universiti Malaya, 50603, Kuala Lumpur, Malaysia.
School of Mathematical Sciences, Universiti Sains Malaysia, 11800, Penang, Malaysia.
Heliyon. 2024 Aug 21;10(17):e36577. doi: 10.1016/j.heliyon.2024.e36577. eCollection 2024 Sep 15.
With the popularization of smart mobile terminals and social media, a large amount of data containing textual information about the city has been generated on social media platforms, covering all areas of the city. This provides a new way for the study of comprehensive perception of city image. In the Internet era, users express their opinions about cities through social media platforms (e.g., Sina Weibo), and mining this information helps to understand the image of cities on mainstream social media and to target positive images to improve the competitiveness of the city's image. In this paper, 370,000 microblog messages related to "Guangzhou City" between 2019 and 2023 are collected using web crawler technology, and three typical text analysis methods are adopted: Term Frequency-Inverse Document Frequency (TF-IDF), Latent Dirichlet Allocation (LDA), and Sentiment Analysis (SnowNLP), to understand the characteristics of Guangzhou city image. gain an in-depth understanding of Guangzhou's urban image characteristics. The study shows that extensive data analysis methods based on text mining can perceive the dynamics and trends of the city in a timely manner, refine the characteristics of Guangzhou's urban image, and propose communication strategies for Guangzhou's image. This study aims to mine Guangzhou's urban image presented on Weibo, provide data support for relevant departments in China and Guangzhou to formulate communication strategies, and provide references for other cities to manage their urban image.
随着智能移动终端和社交媒体的普及，社交媒体平台上产生了大量包含城市文本信息的数据，涵盖城市的各个领域。这为城市形象综合感知研究提供了新途径。在互联网时代，用户通过社交媒体平台（如新浪微博）表达对城市的看法，挖掘这些信息有助于了解主流社交媒体上的城市形象，并针对积极形象提升城市形象竞争力。本文利用网络爬虫技术收集了2019年至2023年期间与“广州市”相关的37万条微博信息，并采用了三种典型的文本分析方法：词频-逆文档频率（TF-IDF）、潜在狄利克雷分配（LDA）和情感分析（SnowNLP），以了解广州市形象特征。深入了解广州城市形象特征。研究表明，基于文本挖掘的广泛数据分析方法能够及时感知城市的动态和趋势，提炼广州城市形象特征，并提出广州形象传播策略。本研究旨在挖掘微博上呈现的广州城市形象，为中国和广州相关部门制定传播策略提供数据支持，并为其他城市管理城市形象提供参考。
