Next Article in Journal
Assessing Port Connectivity from the Perspective of the Supply Chain: A Bayesian Network-Based Integrated Approach
Previous Article in Journal
Is the Energy Quota Trading Policy a Solution to the Decarbonization of Energy Consumption in China?
 
 
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

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

by
Ewerton Chaves Moreira Torres
* and
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
*
Author to whom correspondence should be addressed.
Sustainability 2025, 17(14), 6645; https://doi.org/10.3390/su17146645
Submission received: 16 June 2025 / Revised: 14 July 2025 / Accepted: 16 July 2025 / Published: 21 July 2025
(This article belongs to the Section Sustainable Transportation)

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 sustainable mobility; behavioral change; machine learning; sentiment analysis; university campuses; supervised models

Share and Cite

MDPI and ACS Style

Torres, E.C.M.; de Picado-Santos, L.G. Using Sentiment Analysis to Study the Potential for Improving Sustainable Mobility in University Campuses. Sustainability 2025, 17, 6645. https://doi.org/10.3390/su17146645

AMA Style

Torres ECM, de Picado-Santos LG. Using Sentiment Analysis to Study the Potential for Improving Sustainable Mobility in University Campuses. Sustainability. 2025; 17(14):6645. https://doi.org/10.3390/su17146645

Chicago/Turabian Style

Torres, Ewerton Chaves Moreira, and Luís Guilherme de Picado-Santos. 2025. "Using Sentiment Analysis to Study the Potential for Improving Sustainable Mobility in University Campuses" Sustainability 17, no. 14: 6645. https://doi.org/10.3390/su17146645

APA Style

Torres, E. C. M., & de Picado-Santos, L. G. (2025). Using Sentiment Analysis to Study the Potential for Improving Sustainable Mobility in University Campuses. Sustainability, 17(14), 6645. https://doi.org/10.3390/su17146645

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Article metric data becomes available approximately 24 hours after publication online.
Back to TopTop