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arXiv cs.CL
arXiv cs.CL
6/23/2026
From Sentiment to Actionable Insights: A Data-Driven Public Sentiment Analysis of Advanced Air Mobility

From Sentiment to Actionable Insights: A Data-Driven Public Sentiment Analysis of Advanced Air Mobility

Short summary

Researchers analyzed 306K Reddit/Quora posts on Advanced Air Mobility using AI sentiment models to understand public acceptance barriers. ModernBERT achieved best classification, identifying six concern clusters: workforce/skills (25%), regulation (25%), drone performance (21%), military/geopolitics (15%), safety (9%), and noise (6%). The study provides actionable strategies to improve AAM adoption and public support.

  • •Analyzed 306K social media posts on Advanced Air Mobility sentiment using 7 NLP models
  • •ModernBERT outperformed other approaches; identified 20 topics grouped into 6 major concern clusters
  • •Provides evidence-based adoption strategies addressing workforce, regulation, technical, geopolitical, safety, and noise concerns

Generated with AI, which can make mistakes.

#research-breakthrough#industry-adoption#ai-tools
Read full article at arXiv cs.CL

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