Exploring Cognitive Sustainability Concerns in Public Responses to Extreme Weather Events: An NLP Analysis of Twitter Data

Rihem Berbère (), Safa Elkefi, Safa Bhar Layeb and Achraf Tounsi
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Rihem Berbère: National Engineering School of Tunis, University of Tunis ElManar, Tunis, Tunisia

Cognitive Sustainability, 2023, vol. 2, issue 4, 42-54

Abstract: The United States has a long history of experiencing extreme weather events. Hurricanes are among the most devastating natural disasters that have significant economic and physical impacts on the country. By applying Natural Language Processing (NLP) to Twitter data for sentiment analysis, emotion detection, and topic modelling, this study provides a more thorough understanding of public response and concerns during five study cases of hurricanes that hit the United States: Harvey, Irma, Maria, Ida, and Ian. The findings on sentiment analysis revealed that 64.75% of the tweets were classified as Negative and 35.25% as Positive. For emotion detection, the predominant emotion was anger, with 39.91%. These results were centred around the main public concerns shown by the topic modelling: hurricane management, donation and support, and disaster impacts. Our future work will focus on understanding people’s responses to extreme weather events through the evolving concept of Cognitive Sustainability.

Keywords: Hurricane; people response; Sentiment Analysis; Emotion detection; Topic modeling; Natural Language Processing (search for similar items in EconPapers)
JEL-codes: D71 Z13 (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:bcy:issued:cognitivesustainability:v:2:y:2023:i:4:p:42-54

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DOI: 10.55343/CogSust.80

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