Mining public perceptions of shared autonomous vehicles in China: Interpreting the temporal evolution of sentiment through urban heterogeneity

The rapid deployment of shared autonomous vehicles (SAVs) in China raises pressing questions about public acceptance, yet systematic evidence on evolving public perceptions remains limited. Existing studies often focus on attitudinal surveys or single-platform analyses, overlooking the spatiotemporal heterogeneity of sentiment dynamics and their structural drivers. This study addresses these gaps by analyzing over 56,000 social media data from Sina Weibo, Xiaohongshu, and Douyin between 2022 and 2025. This study proposes an integrated framework combining various natural language processing (NLP) tools such as latent Dirichlet allocation (LDA), bidirectional encoder representations from transformers (BERT), and text network analysis (TNA) to capture thematic concerns and emotional trajectories.

https://www.sciencedirect.com/science/article/pii/S0965856425003799