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Abstract

The implementation of new performance-based navigation procedures at an East Coast airport in 2016 required the airport authority to step up its engagement with airport community residents. This case study leverages natural language processing to explain changes in the sentiments of airport community residents from 2015 to 2021. Natural language processing algorithms made it possible to create a community engagement grid that highlights issues identified in digital prints and social media and allows decision-makers to prioritize them based on awareness and urgency.

Keywords

aviation airspace design machine learning community engagement

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Can We Leverage Text Analysis to Inform Policy Priorities Through a Community Engagement Grid?

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