Leveraging Artificial Intelligence to Analyze Citizens' Opinions on Urban Green Space

TL;DR

This paper introduces an AI-based method using opinion mining and text classification of platform reviews to assess urban green space quality, offering a novel, scalable alternative to traditional expert assessments.

Contribution

It presents a new approach leveraging AI and NLP techniques to analyze citizen opinions from online reviews for urban green space evaluation.

Findings

AI-based opinion mining correlates with expert assessments

Method provides scalable, real-time insights into green space quality

Supports decision-making for urban planning investments

Abstract

Continued population growth and urbanization is shifting research to consider the quality of urban green space over the quantity of these parks, woods, and wetlands. The quality of urban green space has been hitherto measured by expert assessments, including in-situ observations, surveys, and remote sensing analyses. Location data platforms, such as TripAdvisor, can provide people's opinion on many destinations and experiences, including UGS. This paper leverages Artificial Intelligence techniques for opinion mining and text classification using such platform's reviews as a novel approach to urban green space quality assessments. Natural Language Processing is used to analyze contextual information given supervised scores of words by implementing computational analysis. Such an application can support local authorities and stakeholders in their understanding of and justification for future investments in urban green space.