Cardiff University | Prifysgol Caerdydd ORCA
Online Research @ Cardiff 
WelshClear Cookie - decide language by browser settings

Assessing post-disaster recovery using sentiment analysis: The case of L’Aquila, Italy

Contreras Mojica, Diana, Wilkinson, Sean, Balan, Nipun and James, Philip 2022. Assessing post-disaster recovery using sentiment analysis: The case of L’Aquila, Italy. Earthquake Spectra 38 (1) , pp. 81-108. 10.1177/87552930211036486

[thumbnail of 87552930211036486.pdf] PDF - Published Version
Available under License Creative Commons Attribution.

Download (4MB)


Memorial days of disasters represent an opportunity to evaluate the progress of recovery. This article uses sentiment analysis (SA) to assess post-disaster recovery on the 10th anniversary of L’Aquila’s earthquake using Twitter data. We have analyzed 4349 tweets from 4 to 10 April 2019 with the hashtag: #L’Aquila that we have obtained from a third-party vendor. The polarity is first defined using a supervised classification based on experts’ rules on post-disaster reconstruction and Grammarly tones. Then, this polarity is compared with the outcome of an unsupervised classification based on the pre-trained SA machine learning algorithm developed by MonkeyLearn. We have found a significant negative assessment of the post-disaster recovery process in L’Aquila. About 33.1% of the tweets had a negative polarity, followed by 29.3% tweets with a neutral polarity, 28.7% with positive polarity, and 8.9% unrelated to the anniversary. Further analysis of the tweets confirms that after 10 years, the reconstruction is still ongoing and that criticism of the recovery reported in the literature is also found in the tweets. Based on our analysis, the critical day to collect most of the data is the anniversary’s exact day. Tweets from citizens and/or news agencies, which are more likely to express the reality experienced, are therefore more useful in understanding recovery than tweets from government officials and/or governmental institutions. From the total 4349 tweets, we can state that 2488 (57%) were correctly classified by the pre-trained SA machine learning algorithm developed by MonkeyLearn, while 1861 (43%) were misclassified. It means an overall accuracy (ACC) of 57% and a misclassification rate of 43% by the algorithm. We argue that our results have the potential to serve as a benchmark that can be used to compare other post-disaster recovery processes using the same Twitter-based SA on their anniversaries.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Earth and Environmental Sciences
Subjects: G Geography. Anthropology. Recreation > G Geography (General)
G Geography. Anthropology. Recreation > GB Physical geography
G Geography. Anthropology. Recreation > GE Environmental Sciences
H Social Sciences > H Social Sciences (General)
H Social Sciences > HA Statistics
H Social Sciences > HV Social pathology. Social and public welfare
Additional Information: This article is distributed under the terms of the Creative Commons Attribution 4.0 Lficense ( which permits any use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages.
Publisher: SAGE
ISSN: 8755-2930
Funders: Funder: Engineering and Physical Sciences Research Council (EPSRC) Grant number: EP/P025641/1
Related URLs:
Date of First Compliant Deposit: 16 August 2021
Date of Acceptance: 7 July 2021
Last Modified: 11 May 2023 20:57

Citation Data

Cited 3 times in Scopus. View in Scopus. Powered By Scopus® Data

Actions (repository staff only)

Edit Item Edit Item


Downloads per month over past year

View more statistics