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Volume 17
Issue 11
10.3390/su17114967
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This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

Post-Disaster Recovery Assessment Using Sentiment Analysis of English-Language Tweets: A Tenth-Anniversary Case Study of the 2010 Haiti Earthquake

by
Diana Contreras
1,*,
Dimosthenis Antypas
2,
Javier Hervas
3,
Sean Wilkinson
4,
Jose Camacho-Collados
2,
Philippe Garnier
5 and
Cécile Cornou
6
1
School of Earth and Environmental Sciences, Cardiff University, Cardiff CF10 3AT, UK
2
School of Computer Sciences;Cardiff University, Cardiff CF24 4A, UK
3
Independent Researcher, Cardiff CF10 2HS, UK
4
School of Engineering; Newcastle University, Newcastle upon Tyne NE1 7RU, UK
5
AE&CC Research Unit, CRAterre Research Lab, École Nationale Supérieure d'Architecture de Grenoble, Université Grenoble Alpes, 12636 Grenoble, France
6
ISTerre, IRD, Université Grenoble Alpes, 38000 Grenoble, France
*
Author to whom correspondence should be addressed.
Sustainability 2025, 17(11), 4967; https://doi.org/10.3390/su17114967
Submission received: 29 December 2024 / Revised: 29 April 2025 / Accepted: 2 May 2025 / Published: 28 May 2025
(This article belongs to the Section Hazards and Sustainability)
Versions Notes

Abstract

The 2010 Haiti earthquake stands as one of the most catastrophic events in terms of loss of life and destruction. Following an earthquake, there is an urgent demand for information. Regrettably, few studies have tracked the progress of the post-disaster recovery, leaving this phase poorly understood. In previous years, data were exclusively collected through on-site missions, but today, social media (SM) has enhanced earthquake reconnaissance teams’ capacity to collect data beyond the emergency phase. However, text data from SM is unstructured, making it necessary to use natural language processing techniques to extract meaningful information. Sentiment analysis (SA), which classifies people’s opinions into positive, negative, or neutral polarity, is a promising tool for understanding earthquake recovery. For the purposes of this paper, we conduct SA at the tweet level on data collected around the tenth anniversary of the earthquake using human expertise to fine-tune automatic classification methods. We conclude that the anniversary date is the best time to collect data. In our sample, 56.3% of the tweets in the sample were classified as negative, followed by positive (27.3%), neutral (8.2%), and unrelated (8.1%). In our study, we conclude that the assessment of the recovery progress based on data collected from Twitter is negative. The automatic method for SA with the highest accuracy is ‘btweet’. The assessment result must be validated by stakeholders.
Keywords: Haiti; earthquakes; post-disaster recovery; social media (SM); Twitter; natural language processing (NLP); sentiment analysis (SA); reconstruction; vulnerability; funding mismanagement Haiti; earthquakes; post-disaster recovery; social media (SM); Twitter; natural language processing (NLP); sentiment analysis (SA); reconstruction; vulnerability; funding mismanagement

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MDPI and ACS Style

Contreras, D.; Antypas, D.; Hervas, J.; Wilkinson, S.; Camacho-Collados, J.; Garnier, P.; Cornou, C. Post-Disaster Recovery Assessment Using Sentiment Analysis of English-Language Tweets: A Tenth-Anniversary Case Study of the 2010 Haiti Earthquake. Sustainability 2025, 17, 4967. https://doi.org/10.3390/su17114967

AMA Style

Contreras D, Antypas D, Hervas J, Wilkinson S, Camacho-Collados J, Garnier P, Cornou C. Post-Disaster Recovery Assessment Using Sentiment Analysis of English-Language Tweets: A Tenth-Anniversary Case Study of the 2010 Haiti Earthquake. Sustainability. 2025; 17(11):4967. https://doi.org/10.3390/su17114967

Chicago/Turabian Style

Contreras, Diana, Dimosthenis Antypas, Javier Hervas, Sean Wilkinson, Jose Camacho-Collados, Philippe Garnier, and Cécile Cornou. 2025. "Post-Disaster Recovery Assessment Using Sentiment Analysis of English-Language Tweets: A Tenth-Anniversary Case Study of the 2010 Haiti Earthquake" Sustainability 17, no. 11: 4967. https://doi.org/10.3390/su17114967

APA Style

Contreras, D., Antypas, D., Hervas, J., Wilkinson, S., Camacho-Collados, J., Garnier, P., & Cornou, C. (2025). Post-Disaster Recovery Assessment Using Sentiment Analysis of English-Language Tweets: A Tenth-Anniversary Case Study of the 2010 Haiti Earthquake. Sustainability, 17(11), 4967. https://doi.org/10.3390/su17114967

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