Unlocking Urban Sentiments about 15-Min City through Hashtags

The 15-min city concept promotes active transportation and sustainable lifestyles by ensuring essential amenities within a 15-min radius from residences. While attracting significant interest, concerns about potential limitations on freedom of movement have been raised. This study aimed to gain broad insights into public perceptions and understanding of the 15-min city concept. Text mining and sentiment analysis techniques were applied to extract prevailing sentiments. The findings indicated that positive and negative sentiments were evenly distributed among the collected tweets, suggesting a balanced and diverse range of opinions. These opinions are what helps shape the policy and legislation, especially when prominent individuals with a large online presence endorse or reject a specific idea or theory. To further enhance the analysis, three different machine learning classifiers, namely naïve Bayes, logistic regression, and support vector machine, were employed to classify the sentiments expressed in the tweets. The framework developed in this study and the insights derived from the sentiment analysis offer valuable resources for policymakers and urban planners seeking to comprehend and embrace emerging urban concepts like the 15-min city.