Post-disaster recovery assessment using sentiment analysis of English-language tweets: a tenth-anniversary case study of the 2010 Haiti earthquake

Contreras, Diana, Antypas, Dimosthenis, Hervas, Javier, Wilkinson, Sean, Camacho-Collados, Jose, Garnier, Philippe and Cornou, Cécile 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. 10.3390/su17114967

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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.

Item Type:	Article
Date Type:	Published Online
Status:	Published
Schools:	Schools > Earth and Environmental Sciences Schools > Computer Science & Informatics
Additional Information:	License information from Publisher: LICENSE 1: URL: https://creativecommons.org/licenses/by/4.0/, Start Date: 2025-05-28
Publisher:	MDPI
Date of First Compliant Deposit:	13 June 2025
Date of Acceptance:	2 May 2025
Last Modified:	13 Jun 2025 09:45
URI:	https://orca.cardiff.ac.uk/id/eprint/179063

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