Using Sentiment Analysis to Study the Potential for Improving Sustainable Mobility in University Campuses

Ewerton Chaves Moreira Torres () and Luís Guilherme de Picado-Santos
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Ewerton Chaves Moreira Torres: Civil Engineering Research and Innovation for Sustainability (CERIS), Instituto Superior Técnico, Universidade de Lisboa, Av. Rovisco Pais 1, 1049-001 Lisboa, Portugal
Luís Guilherme de Picado-Santos: Civil Engineering Research and Innovation for Sustainability (CERIS), Instituto Superior Técnico, Universidade de Lisboa, Av. Rovisco Pais 1, 1049-001 Lisboa, Portugal

Sustainability, 2025, vol. 17, issue 14, 1-22

Abstract: This study investigates public perceptions of sustainable mobility within university environments, which are important trip generation hubs with the potential to influence and disseminate sustainable mobility behaviors. Using sentiment analysis on 120,236 tweets from São Paulo, Rio de Janeiro, Lisbon, and Porto, tweets were classified into positive, neutral, and negative sentiments to assess perceptions across transport modes. It was hypothesized that universities would exhibit more positive sentiment toward active and public transport modes compared to perceptions of these modes within the broader city environment. Results show that active modes and public transport consistently receive higher positive sentiment rates than individual motorized modes, and, considering the analyzed contexts, universities demonstrate either similar (São Paulo) or more positive perceptions compared to the overall sentiment observed in the city (Rio de Janeiro, Lisbon, and Porto). Chi-square tests confirmed significant associations between transport mode and sentiment distribution. An exploratory analysis using topic modeling revealed that perceptions around bicycle use are linked to themes of safety, cycling infrastructure, and bike sharing. The findings highlight opportunities to promote sustainable mobility in universities by leveraging user sentiment while acknowledging limitations such as demographic bias in social media data and potential misclassification. This study advances data-driven methods to support targeted strategies for increasing active and public transport in university settings.

Keywords: sustainable mobility; behavioral change; machine learning; sentiment analysis; university campuses; supervised models (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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