交通领域公众兴趣近期激增：利用微博数据对百度Apollo Go 进行情感分析

Recent Surge in Public Interest in Transportation: Sentiment Analysis of Baidu Apollo Go Using Weibo Data

August 19, 2024

作者: Shiqi Wang, Zhouye Zhao, Yuhang Xie, Mingchuan Ma, Zirui Chen, Zeyu Wang, Bohao Su, Wenrui Xu, Tianyi Li

cs.AI

摘要

自动驾驶技术的进步深刻改变了城市交通和运输系统。百度Apollo Go是中国科技巨头百度推出的先驱机器人出租车服务，最近已在北京、武汉等主要城市广泛部署，引发了更多讨论，展示了城市交通未来的一瞥。 本研究利用混合BERT模型对2024年1月至7月的36096条微博帖子进行情感分析，调查了中国公众对Apollo Go的态度。分析显示，89.56\%与Apollo Go相关的帖子集中在7月。从1月到7月，公众情绪大多是积极的，但在7月21日成为热门话题后，负面评论开始增加。 空间分析表明，高讨论强度的省份与Apollo Go运营的省份之间存在强烈相关性。最初，湖北和广东主导了在线发布量，但到了7月，广东、北京和国际地区已超过了湖北。各省的态度存在显著差异，新疆和青海表现出乐观，而西藏和甘肃则对传统出租车服务的影响表示担忧。 情感分析显示，积极评论主要集中在技术应用和个人经验上，而负面评论则主要关注工作岗位流失和安全问题。总之，本研究突显了公众对自动乘车服务的看法分歧，为规划者、政策制定者和服务提供者提供了宝贵的见解。该模型已发布在Hugging Face上，链接为https://huggingface.co/wsqstar/bert-finetuned-weibo-luobokuaipao，并在GitHub上的存储库链接为https://github.com/GIStudio/trb2024。

English

Urban mobility and transportation systems have been profoundly transformed by the advancement of autonomous vehicle technologies. Baidu Apollo Go, a pioneer robotaxi service from the Chinese tech giant Baidu, has recently been widely deployed in major cities like Beijing and Wuhan, sparking increased conversation and offering a glimpse into the future of urban mobility. This study investigates public attitudes towards Apollo Go across China using Sentiment Analysis with a hybrid BERT model on 36,096 Weibo posts from January to July 2024. The analysis shows that 89.56\% of posts related to Apollo Go are clustered in July. From January to July, public sentiment was mostly positive, but negative comments began to rise after it became a hot topic on July 21. Spatial analysis indicates a strong correlation between provinces with high discussion intensity and those where Apollo Go operates. Initially, Hubei and Guangdong dominated online posting volume, but by July, Guangdong, Beijing, and international regions had overtaken Hubei. Attitudes varied significantly among provinces, with Xinjiang and Qinghai showing optimism and Tibet and Gansu expressing concerns about the impact on traditional taxi services. Sentiment analysis revealed that positive comments focused on technology applications and personal experiences, while negative comments centered on job displacement and safety concerns. In summary, this study highlights the divergence in public perceptions of autonomous ride-hailing services, providing valuable insights for planners, policymakers, and service providers. The model is published on Hugging Face at https://huggingface.co/wsqstar/bert-finetuned-weibo-luobokuaipao and the repository on GitHub at https://github.com/GIStudio/trb2024.