分析30万条网络讨论，揭示公众对空中交通的关切点
From Sentiment to Actionable Insights: Public Sentiment Analysis of Advanced Air Mobility
- 用多种AI模型对比筛选出最适合的语义分析方法
- 发现6大核心关注领域，涵盖安全、噪音与政策等
- 为政府和企业制定推广策略提供数据支持
先进空中交通（AAM）作为新兴低空运输系统，其成功部署依赖技术进展与公众接受度。公众态度影响政府支持、法规制定、噪音标准、乘机意愿及商业化前景。本研究基于从Reddit和Quora收集的306,009条人类生成文本，采用人工智能模型分析AAM相关公共话语。评估了七种情感分析方法（词典法、机器学习、深度学习与Transformer模型），ModernBERT表现最优，用于全量数据标注。在此基础上，对每类情感使用隐含狄利克雷分配（LDA）识别潜在主题，并分析2008至2025年的演变趋势。共识别出20个主题，形成六大跨情感聚类：劳动力与技能发展、监管合规、无人机技术性能、军事与地缘政治应用、安全与运营风险、噪音与扰动。研究成果可帮助政策制定者、产业界、研究人员及运营商制定针对性法规、安全措施、人才培养计划、降噪策略与公众沟通方案，推动AAM负责任落地。
原文摘要 · Abstract (English)
Advanced Air Mobility (AAM) is an emerging low-altitude transportation system whose successful deployment depends on both technological progress and public acceptance. Public acceptance can influence government support, regulations, noise standards, willingness to fly, and the commercial viability of AAM. Understanding public sentiment is therefore essential for identifying societal barriers and developing effective adoption strategies. This study analyzes 306,009 human-generated texts collected from Reddit and Quora to examine AAM-related public discourse using artificial intelligence models. Seven sentiment-analysis approaches, including lexicon-based, machine-learning, deep-learning, and transformer models, are evaluated to identify the most reliable method for AAM-specific sentiment classification. ModernBERT achieves the highest performance and is used to label the full dataset. Latent Dirichlet Allocation is then applied within each sentiment class to identify underlying topics and examine their temporal evolution from 2008 to 2025. The analysis identifies 20 topics and six major cross-sentiment clusters: workforce and skill development, regulation and compliance, drone technical performance, military and geopolitical applications, safety and operational risks, and noise and disturbance. These findings can help policymakers, industry stakeholders, researchers, and operators develop targeted regulations, safety measures, workforce programs, noise-reduction strategies, and public communication efforts to address concerns and support the responsible deployment of AAM.
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