Analysis of Guangzhou city image perception based on weibo text data (2019–2023)

Abstract

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.

Keywords

• City image
• Social media
• Text mining
• Natural language processing