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

2026-06-17

AI Summary

This study addresses public acceptance of Advanced Air Mobility (AAM) by systematically evaluating the applicability of seven sentiment analysis models on a corpus of over 300,000 Reddit and Quora posts. ModernBERT was selected for high-precision sentiment annotation and integrated with Latent Dirichlet Allocation (LDA) topic modeling to uncover six dominant concern clusters underlying public sentiment from 2008 to 2025: workforce development, regulatory compliance, unmanned aircraft performance, military geopolitics, safety risks, and noise disturbance—collectively accounting for 99.99% of the data. By innovatively combining sentiment analysis with thematic evolution, this work not only elucidates evolving public perceptions but also formulates targeted policy communication and deployment strategies to enhance societal readiness for AAM integration.

Abstract

Advanced Air Mobility (AAM) is an emerging low-altitude air transportation system whose successful deployment depends not only on technological advancement but also on public acceptance. This acceptance will drive government support, regulations, noise standards, and willingness to fly, and in turn the overall commercial viability of AAM. Understanding public sentiment toward AAM is therefore essential for identifying its societal barriers and informing its adoption strategies. This study analyzes 306,009 human-generated texts collected from Reddit and Quora to examine public discourse on AAM using AI-based models. Because multiple sentiment analysis models exist, identifying the most accurate model is critical for reliable AAM sentiment prediction and trustworthy public opinion analysis. Accordingly, seven models spanning lexicon-based, machine learning, deep learning, and transformer-based approaches are evaluated for AAM-specific sentiment classification. ModernBERT achieves the best classification performance and is used to label the full dataset. Using the resulting sentiment labels, Latent Dirichlet Allocation (LDA) is applied within each sentiment class to uncover latent topics in public opinion. The analysis identifies 20 distinct topics and traces their temporal evolution from 2008 to 2025. A cross-sentiment topic analysis further reveals six major clusters of public concern: workforce and skill development (25.29% of the dataset), regulation and compliance (24.64%), technical performance of drones (20.99%), military, geopolitics, and defense (14.58%), safety and operational risks (8.51%), and noise and disturbance (5.98%). Based on these findings, this study provides actionable strategies to address these concerns, thereby, improving public acceptance and support AAM deployment.

Problem

• public sentiment
• Advanced Air Mobility
• sentiment analysis
• social acceptance
• public opinion

Innovation

• sentiment analysis
• topic modeling
• Advanced Air Mobility
• ModernBERT
• public acceptance