Public Emotional Response and Theme Mining of Emerging Technologies: An Empirical Study Based on the "RoboTaxi" Autonomous Driving Platform

With the rapid advancement of artificial intelligence and autonomous driving technology, plat-forms like Baidu's "RoboTaxi" are emerging as key innovations in smart transportation. However, concerns about public acceptance, risk perception, and the maturity of this technology continue to hinder widespread adoption. This study investigates user responses to the "RoboTaxi" platform by analyzing 9,235 comments collected from the Douyin platform. Using Python’s automated web crawling for data collection, we employed the BERTopic model for core theme identification and the RoBERTa model for sentiment classification. The results reveal significant emotional dy-namics, with 70.3% of comments expressing negative emotions, mainly due to safety concerns and accountability issues, and 21.6% reflecting positive sentiments related to innovation and fu-ture potential. Four main themes were identified: societal impacts, safety and responsibility, prac-ticality and design, and economic benefits. These findings offer important insights for improving public trust, optimizing platform operations, and guiding future strategies for the successful pro-motion of autonomous driving technology.