This study aims to explore user acceptance of Autonomous Vehicle (AV) policies with improved text-mining methods. Recently, South Korean policymakers have viewed Autonomous Driving Car (ADC) and Autonomous Driving Robot (ADR) as next-generation means of transportation that will reduce the cost of transporting passengers and goods. They support the construction of V2I and V2V communication infrastructures for ADC and recognize that ADR is equivalent to pedestrians to promote its deployment into sidewalks. To fill the gap where end-user acceptance of these policies is not well considered, this study applied two text-mining methods to the comments of graduate students in the fields of Industrial, Mechanical, and Electronics-Electrical-Computer. One is the Co-occurrence Network Analysis (CNA) based on TF-IWF and Dice coefficient, and the other is the Contextual Semantic Network Analysis (C-SNA) based on both KeyBERT, which extracts keywords that contextually represent the comments, and double cosine similarity. The reason for comparing these approaches is to balance interest not only in the implications for the AV policies but also in the need to apply quality text mining to this research domain. Significantly, the limitation of frequency-based text mining, which does not reflect textual context, and the trade-off of adjusting thresholds in Semantic Network Analysis (SNA) were considered. As the results of comparing the two approaches, the C-SNA provided the information necessary to understand users' voices using fewer nodes and features than the CNA. The users who pre-emptively understood the AV policies based on their engineering literacy and the given texts revealed potential risks of the AV accident policies. This study adds suggestions to manage these risks to support the successful deployment of AVs on public roads.

翻译：本研究旨在运用改进的文本挖掘方法，探究用户对自动驾驶汽车（AV）政策的接受度。近期，韩国政策制定者将自动驾驶汽车（ADC）和自动驾驶机器人（ADR）视为能降低客货运输成本的下一代交通工具。他们支持建设适用于ADC的车对基础设施（V2I）与车对车（V2V）通信基础设施，并将ADR等同于行人以推动其在人行道上的部署。为弥补终端用户对这些政策接受度未得到充分考虑的研究空白，本研究对工业工程、机械工程及电子-电气-计算机工程领域的研究生评论采用了两种文本挖掘方法：其一是基于TF-IWF与Dice系数的共现网络分析（CNA），其二是基于语境化表征评论关键词的KeyBERT与双余弦相似度的上下文语义网络分析（C-SNA）。比较这两种方法的目的在于平衡对AV政策内涵的关注与在该研究领域中应用高质量文本挖掘方法的需求。特别地，本研究考虑了未反映文本语境的基于频率的文本挖掘的局限性，以及语义网络分析（SNA）中调整阈值的权衡效应。通过比较两种方法的结果发现：C-SNA能以比CNA更少的节点和特征提供理解用户声音所需的信息。凭借工程素养及给定文本而预先理解AV政策的用户，揭示了AV事故政策的潜在风险。本研究为成功部署公共道路上的AV提出了管理这些风险的建议。