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

Author

Listed:
  • Diana Contreras

    (School of Earth and Environmental Sciences, Cardiff University, Cardiff CF10 3AT, UK)

  • Dimosthenis Antypas

    (School of Computer Sciences, Cardiff University, Cardiff CF24 4A, UK)

  • Javier Hervas

    (Independent Researcher, Cardiff CF10 2HS, UK)

  • Sean Wilkinson

    (School of Engineering, Newcastle University, Newcastle upon Tyne NE1 7RU, UK)

  • Jose Camacho-Collados

    (School of Computer Sciences, Cardiff University, Cardiff CF24 4A, UK)

  • Philippe Garnier

    (AE&CC Research Unit, CRAterre Research Lab, École Nationale Supérieure d’Architecture de Grenoble, Université Grenoble Alpes, 12636 Grenoble, France)

  • Cécile Cornou

    (ISTerre, IRD, Université Grenoble Alpes, 38000 Grenoble, France)

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.

Suggested Citation

  • Diana Contreras & Dimosthenis Antypas & Javier Hervas & Sean Wilkinson & Jose Camacho-Collados & Philippe Garnier & 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, MDPI, vol. 17(11), pages 1-32, May.
    • Handle: RePEc:gam:jsusta:v:17:y:2025:i:11:p:4967-:d:1666698
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    References listed on IDEAS

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    1. Yates, Dave & Paquette, Scott, 2011. "Emergency knowledge management and social media technologies: A case study of the 2010 Haitian earthquake," International Journal of Information Management, Elsevier, vol. 31(1), pages 6-13.
    2. Simon, Tomer & Goldberg, Avishay & Adini, Bruria, 2015. "Socializing in emergencies—A review of the use of social media in emergency situations," International Journal of Information Management, Elsevier, vol. 35(5), pages 609-619.
    3. Hongzhou Shen & Yue Ju & Zhijing Zhu, 2023. "Extracting Useful Emergency Information from Social Media: A Method Integrating Machine Learning and Rule-Based Classification," IJERPH, MDPI, vol. 20(3), pages 1-20, January.
    4. Laura Zanotti, 2010. "Cacophonies of Aid, Failed State Building and s in Haiti: setting the stage for disaster, envisioning the future," Third World Quarterly, Taylor & Francis Journals, vol. 31(5), pages 755-771.
    5. Ragini, J. Rexiline & Anand, P.M. Rubesh & Bhaskar, Vidhyacharan, 2018. "Big data analytics for disaster response and recovery through sentiment analysis," International Journal of Information Management, Elsevier, vol. 42(C), pages 13-24.
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