Accuracy of a pre-trained sentiment analysis (SA) classification model on tweets related to emergency response and early recovery assessment: the case of 2019 Albanian earthquake

Abstract Traditionally, earthquake impact assessments have been made via fieldwork by non-governmental organisations (NGO's) sponsored data collection; however, this approach is time-consuming, expensive and often limited. Recently, social media (SM) has become a valuable tool for quickly colle...
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Autor*in:	Contreras, Diana [verfasserIn] Wilkinson, Sean Alterman, Evangeline Hervás, Javier

Format:	Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	Earthquakes Social media (SM) Twitter Sentiment analysis (SA) Machine learning algorithm Accuracy (ACC)

Anmerkung:	© The Author(s) 2022

Übergeordnetes Werk:	Enthalten in: Natural hazards - Springer Netherlands, 1988, 113(2022), 1 vom: 23. März, Seite 403-421
Übergeordnetes Werk:	volume:113 ; year:2022 ; number:1 ; day:23 ; month:03 ; pages:403-421

Links:	Volltext

DOI / URN:	10.1007/s11069-022-05307-w

Katalog-ID:	OLC2079247298

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520			\|a Abstract Traditionally, earthquake impact assessments have been made via fieldwork by non-governmental organisations (NGO's) sponsored data collection; however, this approach is time-consuming, expensive and often limited. Recently, social media (SM) has become a valuable tool for quickly collecting large amounts of first-hand data after a disaster and shows great potential for decision-making. Nevertheless, extracting meaningful information from SM is an ongoing area of research. This paper tests the accuracy of the pre-trained sentiment analysis (SA) model developed by the no-code machine learning platform MonkeyLearn using the text data related to the emergency response and early recovery phase of the three major earthquakes that struck Albania on the 26th November 2019. These events caused 51 deaths, 3000 injuries and extensive damage. We obtained 695 tweets with the hashtags: #Albania #AlbanianEarthquake, and #albanianearthquake from the 26th November 2019 to the 3rd February 2020. We used these data to test the accuracy of the pre-trained SA classification model developed by MonkeyLearn to identify polarity in text data. This test explores the feasibility to automate the classification process to extract meaningful information from text data from SM in real-time in the future. We tested the no-code machine learning platform's performance using a confusion matrix. We obtained an overall accuracy (ACC) of 63% and a misclassification rate of 37%. We conclude that the ACC of the unsupervised classification is sufficient for a preliminary assessment, but further research is needed to determine if the accuracy is improved by customising the training model of the machine learning platform.
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allfields	10.1007/s11069-022-05307-w doi (DE-627)OLC2079247298 (DE-He213)s11069-022-05307-w-p DE-627 ger DE-627 rakwb eng 550 VZ 14 ssgn Contreras, Diana verfasserin (orcid)0000-0003-2852-6015 aut Accuracy of a pre-trained sentiment analysis (SA) classification model on tweets related to emergency response and early recovery assessment: the case of 2019 Albanian earthquake 2022 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s) 2022 Abstract Traditionally, earthquake impact assessments have been made via fieldwork by non-governmental organisations (NGO's) sponsored data collection; however, this approach is time-consuming, expensive and often limited. Recently, social media (SM) has become a valuable tool for quickly collecting large amounts of first-hand data after a disaster and shows great potential for decision-making. Nevertheless, extracting meaningful information from SM is an ongoing area of research. This paper tests the accuracy of the pre-trained sentiment analysis (SA) model developed by the no-code machine learning platform MonkeyLearn using the text data related to the emergency response and early recovery phase of the three major earthquakes that struck Albania on the 26th November 2019. These events caused 51 deaths, 3000 injuries and extensive damage. We obtained 695 tweets with the hashtags: #Albania #AlbanianEarthquake, and #albanianearthquake from the 26th November 2019 to the 3rd February 2020. We used these data to test the accuracy of the pre-trained SA classification model developed by MonkeyLearn to identify polarity in text data. This test explores the feasibility to automate the classification process to extract meaningful information from text data from SM in real-time in the future. We tested the no-code machine learning platform's performance using a confusion matrix. We obtained an overall accuracy (ACC) of 63% and a misclassification rate of 37%. We conclude that the ACC of the unsupervised classification is sufficient for a preliminary assessment, but further research is needed to determine if the accuracy is improved by customising the training model of the machine learning platform. Earthquakes Social media (SM) Twitter Sentiment analysis (SA) Machine learning algorithm Accuracy (ACC) Wilkinson, Sean aut Alterman, Evangeline (orcid)0000-0001-5942-3866 aut Hervás, Javier (orcid)0000-0003-4272-3525 aut Enthalten in Natural hazards Springer Netherlands, 1988 113(2022), 1 vom: 23. März, Seite 403-421 (DE-627)131010271 (DE-600)1088547-X (DE-576)03285272X 0921-030X nnns volume:113 year:2022 number:1 day:23 month:03 pages:403-421 https://doi.org/10.1007/s11069-022-05307-w lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-PHY SSG-OLC-MAT SSG-OPC-GGO SSG-OPC-MAT AR 113 2022 1 23 03 403-421
spelling	10.1007/s11069-022-05307-w doi (DE-627)OLC2079247298 (DE-He213)s11069-022-05307-w-p DE-627 ger DE-627 rakwb eng 550 VZ 14 ssgn Contreras, Diana verfasserin (orcid)0000-0003-2852-6015 aut Accuracy of a pre-trained sentiment analysis (SA) classification model on tweets related to emergency response and early recovery assessment: the case of 2019 Albanian earthquake 2022 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s) 2022 Abstract Traditionally, earthquake impact assessments have been made via fieldwork by non-governmental organisations (NGO's) sponsored data collection; however, this approach is time-consuming, expensive and often limited. Recently, social media (SM) has become a valuable tool for quickly collecting large amounts of first-hand data after a disaster and shows great potential for decision-making. Nevertheless, extracting meaningful information from SM is an ongoing area of research. This paper tests the accuracy of the pre-trained sentiment analysis (SA) model developed by the no-code machine learning platform MonkeyLearn using the text data related to the emergency response and early recovery phase of the three major earthquakes that struck Albania on the 26th November 2019. These events caused 51 deaths, 3000 injuries and extensive damage. We obtained 695 tweets with the hashtags: #Albania #AlbanianEarthquake, and #albanianearthquake from the 26th November 2019 to the 3rd February 2020. We used these data to test the accuracy of the pre-trained SA classification model developed by MonkeyLearn to identify polarity in text data. This test explores the feasibility to automate the classification process to extract meaningful information from text data from SM in real-time in the future. We tested the no-code machine learning platform's performance using a confusion matrix. We obtained an overall accuracy (ACC) of 63% and a misclassification rate of 37%. We conclude that the ACC of the unsupervised classification is sufficient for a preliminary assessment, but further research is needed to determine if the accuracy is improved by customising the training model of the machine learning platform. Earthquakes Social media (SM) Twitter Sentiment analysis (SA) Machine learning algorithm Accuracy (ACC) Wilkinson, Sean aut Alterman, Evangeline (orcid)0000-0001-5942-3866 aut Hervás, Javier (orcid)0000-0003-4272-3525 aut Enthalten in Natural hazards Springer Netherlands, 1988 113(2022), 1 vom: 23. März, Seite 403-421 (DE-627)131010271 (DE-600)1088547-X (DE-576)03285272X 0921-030X nnns volume:113 year:2022 number:1 day:23 month:03 pages:403-421 https://doi.org/10.1007/s11069-022-05307-w lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-PHY SSG-OLC-MAT SSG-OPC-GGO SSG-OPC-MAT AR 113 2022 1 23 03 403-421
allfields_unstemmed	10.1007/s11069-022-05307-w doi (DE-627)OLC2079247298 (DE-He213)s11069-022-05307-w-p DE-627 ger DE-627 rakwb eng 550 VZ 14 ssgn Contreras, Diana verfasserin (orcid)0000-0003-2852-6015 aut Accuracy of a pre-trained sentiment analysis (SA) classification model on tweets related to emergency response and early recovery assessment: the case of 2019 Albanian earthquake 2022 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s) 2022 Abstract Traditionally, earthquake impact assessments have been made via fieldwork by non-governmental organisations (NGO's) sponsored data collection; however, this approach is time-consuming, expensive and often limited. Recently, social media (SM) has become a valuable tool for quickly collecting large amounts of first-hand data after a disaster and shows great potential for decision-making. Nevertheless, extracting meaningful information from SM is an ongoing area of research. This paper tests the accuracy of the pre-trained sentiment analysis (SA) model developed by the no-code machine learning platform MonkeyLearn using the text data related to the emergency response and early recovery phase of the three major earthquakes that struck Albania on the 26th November 2019. These events caused 51 deaths, 3000 injuries and extensive damage. We obtained 695 tweets with the hashtags: #Albania #AlbanianEarthquake, and #albanianearthquake from the 26th November 2019 to the 3rd February 2020. We used these data to test the accuracy of the pre-trained SA classification model developed by MonkeyLearn to identify polarity in text data. This test explores the feasibility to automate the classification process to extract meaningful information from text data from SM in real-time in the future. We tested the no-code machine learning platform's performance using a confusion matrix. We obtained an overall accuracy (ACC) of 63% and a misclassification rate of 37%. We conclude that the ACC of the unsupervised classification is sufficient for a preliminary assessment, but further research is needed to determine if the accuracy is improved by customising the training model of the machine learning platform. Earthquakes Social media (SM) Twitter Sentiment analysis (SA) Machine learning algorithm Accuracy (ACC) Wilkinson, Sean aut Alterman, Evangeline (orcid)0000-0001-5942-3866 aut Hervás, Javier (orcid)0000-0003-4272-3525 aut Enthalten in Natural hazards Springer Netherlands, 1988 113(2022), 1 vom: 23. März, Seite 403-421 (DE-627)131010271 (DE-600)1088547-X (DE-576)03285272X 0921-030X nnns volume:113 year:2022 number:1 day:23 month:03 pages:403-421 https://doi.org/10.1007/s11069-022-05307-w lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-PHY SSG-OLC-MAT SSG-OPC-GGO SSG-OPC-MAT AR 113 2022 1 23 03 403-421
allfieldsGer	10.1007/s11069-022-05307-w doi (DE-627)OLC2079247298 (DE-He213)s11069-022-05307-w-p DE-627 ger DE-627 rakwb eng 550 VZ 14 ssgn Contreras, Diana verfasserin (orcid)0000-0003-2852-6015 aut Accuracy of a pre-trained sentiment analysis (SA) classification model on tweets related to emergency response and early recovery assessment: the case of 2019 Albanian earthquake 2022 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s) 2022 Abstract Traditionally, earthquake impact assessments have been made via fieldwork by non-governmental organisations (NGO's) sponsored data collection; however, this approach is time-consuming, expensive and often limited. Recently, social media (SM) has become a valuable tool for quickly collecting large amounts of first-hand data after a disaster and shows great potential for decision-making. Nevertheless, extracting meaningful information from SM is an ongoing area of research. This paper tests the accuracy of the pre-trained sentiment analysis (SA) model developed by the no-code machine learning platform MonkeyLearn using the text data related to the emergency response and early recovery phase of the three major earthquakes that struck Albania on the 26th November 2019. These events caused 51 deaths, 3000 injuries and extensive damage. We obtained 695 tweets with the hashtags: #Albania #AlbanianEarthquake, and #albanianearthquake from the 26th November 2019 to the 3rd February 2020. We used these data to test the accuracy of the pre-trained SA classification model developed by MonkeyLearn to identify polarity in text data. This test explores the feasibility to automate the classification process to extract meaningful information from text data from SM in real-time in the future. We tested the no-code machine learning platform's performance using a confusion matrix. We obtained an overall accuracy (ACC) of 63% and a misclassification rate of 37%. We conclude that the ACC of the unsupervised classification is sufficient for a preliminary assessment, but further research is needed to determine if the accuracy is improved by customising the training model of the machine learning platform. Earthquakes Social media (SM) Twitter Sentiment analysis (SA) Machine learning algorithm Accuracy (ACC) Wilkinson, Sean aut Alterman, Evangeline (orcid)0000-0001-5942-3866 aut Hervás, Javier (orcid)0000-0003-4272-3525 aut Enthalten in Natural hazards Springer Netherlands, 1988 113(2022), 1 vom: 23. März, Seite 403-421 (DE-627)131010271 (DE-600)1088547-X (DE-576)03285272X 0921-030X nnns volume:113 year:2022 number:1 day:23 month:03 pages:403-421 https://doi.org/10.1007/s11069-022-05307-w lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-PHY SSG-OLC-MAT SSG-OPC-GGO SSG-OPC-MAT AR 113 2022 1 23 03 403-421
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author	Contreras, Diana
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topic_title	550 VZ 14 ssgn Accuracy of a pre-trained sentiment analysis (SA) classification model on tweets related to emergency response and early recovery assessment: the case of 2019 Albanian earthquake Earthquakes Social media (SM) Twitter Sentiment analysis (SA) Machine learning algorithm Accuracy (ACC)
topic	ddc 550 ssgn 14 misc Earthquakes misc Social media (SM) misc Twitter misc Sentiment analysis (SA) misc Machine learning algorithm misc Accuracy (ACC)
topic_unstemmed	ddc 550 ssgn 14 misc Earthquakes misc Social media (SM) misc Twitter misc Sentiment analysis (SA) misc Machine learning algorithm misc Accuracy (ACC)
topic_browse	ddc 550 ssgn 14 misc Earthquakes misc Social media (SM) misc Twitter misc Sentiment analysis (SA) misc Machine learning algorithm misc Accuracy (ACC)
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title	Accuracy of a pre-trained sentiment analysis (SA) classification model on tweets related to emergency response and early recovery assessment: the case of 2019 Albanian earthquake
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title_full	Accuracy of a pre-trained sentiment analysis (SA) classification model on tweets related to emergency response and early recovery assessment: the case of 2019 Albanian earthquake
author_sort	Contreras, Diana
journal	Natural hazards
journalStr	Natural hazards
lang_code	eng
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author_browse	Contreras, Diana Wilkinson, Sean Alterman, Evangeline Hervás, Javier
container_volume	113
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author-letter	Contreras, Diana
doi_str_mv	10.1007/s11069-022-05307-w
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title_sort	accuracy of a pre-trained sentiment analysis (sa) classification model on tweets related to emergency response and early recovery assessment: the case of 2019 albanian earthquake
title_auth	Accuracy of a pre-trained sentiment analysis (SA) classification model on tweets related to emergency response and early recovery assessment: the case of 2019 Albanian earthquake
abstract	Abstract Traditionally, earthquake impact assessments have been made via fieldwork by non-governmental organisations (NGO's) sponsored data collection; however, this approach is time-consuming, expensive and often limited. Recently, social media (SM) has become a valuable tool for quickly collecting large amounts of first-hand data after a disaster and shows great potential for decision-making. Nevertheless, extracting meaningful information from SM is an ongoing area of research. This paper tests the accuracy of the pre-trained sentiment analysis (SA) model developed by the no-code machine learning platform MonkeyLearn using the text data related to the emergency response and early recovery phase of the three major earthquakes that struck Albania on the 26th November 2019. These events caused 51 deaths, 3000 injuries and extensive damage. We obtained 695 tweets with the hashtags: #Albania #AlbanianEarthquake, and #albanianearthquake from the 26th November 2019 to the 3rd February 2020. We used these data to test the accuracy of the pre-trained SA classification model developed by MonkeyLearn to identify polarity in text data. This test explores the feasibility to automate the classification process to extract meaningful information from text data from SM in real-time in the future. We tested the no-code machine learning platform's performance using a confusion matrix. We obtained an overall accuracy (ACC) of 63% and a misclassification rate of 37%. We conclude that the ACC of the unsupervised classification is sufficient for a preliminary assessment, but further research is needed to determine if the accuracy is improved by customising the training model of the machine learning platform. © The Author(s) 2022
abstractGer	Abstract Traditionally, earthquake impact assessments have been made via fieldwork by non-governmental organisations (NGO's) sponsored data collection; however, this approach is time-consuming, expensive and often limited. Recently, social media (SM) has become a valuable tool for quickly collecting large amounts of first-hand data after a disaster and shows great potential for decision-making. Nevertheless, extracting meaningful information from SM is an ongoing area of research. This paper tests the accuracy of the pre-trained sentiment analysis (SA) model developed by the no-code machine learning platform MonkeyLearn using the text data related to the emergency response and early recovery phase of the three major earthquakes that struck Albania on the 26th November 2019. These events caused 51 deaths, 3000 injuries and extensive damage. We obtained 695 tweets with the hashtags: #Albania #AlbanianEarthquake, and #albanianearthquake from the 26th November 2019 to the 3rd February 2020. We used these data to test the accuracy of the pre-trained SA classification model developed by MonkeyLearn to identify polarity in text data. This test explores the feasibility to automate the classification process to extract meaningful information from text data from SM in real-time in the future. We tested the no-code machine learning platform's performance using a confusion matrix. We obtained an overall accuracy (ACC) of 63% and a misclassification rate of 37%. We conclude that the ACC of the unsupervised classification is sufficient for a preliminary assessment, but further research is needed to determine if the accuracy is improved by customising the training model of the machine learning platform. © The Author(s) 2022
abstract_unstemmed	Abstract Traditionally, earthquake impact assessments have been made via fieldwork by non-governmental organisations (NGO's) sponsored data collection; however, this approach is time-consuming, expensive and often limited. Recently, social media (SM) has become a valuable tool for quickly collecting large amounts of first-hand data after a disaster and shows great potential for decision-making. Nevertheless, extracting meaningful information from SM is an ongoing area of research. This paper tests the accuracy of the pre-trained sentiment analysis (SA) model developed by the no-code machine learning platform MonkeyLearn using the text data related to the emergency response and early recovery phase of the three major earthquakes that struck Albania on the 26th November 2019. These events caused 51 deaths, 3000 injuries and extensive damage. We obtained 695 tweets with the hashtags: #Albania #AlbanianEarthquake, and #albanianearthquake from the 26th November 2019 to the 3rd February 2020. We used these data to test the accuracy of the pre-trained SA classification model developed by MonkeyLearn to identify polarity in text data. This test explores the feasibility to automate the classification process to extract meaningful information from text data from SM in real-time in the future. We tested the no-code machine learning platform's performance using a confusion matrix. We obtained an overall accuracy (ACC) of 63% and a misclassification rate of 37%. We conclude that the ACC of the unsupervised classification is sufficient for a preliminary assessment, but further research is needed to determine if the accuracy is improved by customising the training model of the machine learning platform. © The Author(s) 2022
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container_issue	1
title_short	Accuracy of a pre-trained sentiment analysis (SA) classification model on tweets related to emergency response and early recovery assessment: the case of 2019 Albanian earthquake
url	https://doi.org/10.1007/s11069-022-05307-w
remote_bool	false
author2	Wilkinson, Sean Alterman, Evangeline Hervás, Javier
author2Str	Wilkinson, Sean Alterman, Evangeline Hervás, Javier
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doi_str	10.1007/s11069-022-05307-w
up_date	2024-08-27T23:09:48.372Z
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