“The Public Health Contribution of Sentiment Analysis of Monkeypox Tweets to Detect Polarities Using the CNN-LSTM Model“

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“Monkeypox is a rare disease caused by the monkeypox virus. This disease was considered eradicated in 1980 and was believed to affect rodents and not humans. However, recent years have seen a massive outbreak of monkeypox in humans, setting off worldwide alerts from health agencies. As of September...

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Detalles Bibliográficos
Autores: Iparraguirre-Villanueva, Orlando, Alvarez-Risco, Aldo, Herrera Salazar, Jose Luis, Beltozar-Clemente, Saul, Zapata-Paulini, Joselyn, Yáñez, Jaime A., Cabanillas-Carbonell, Michael
Formato: artículo
Fecha de Publicación:2023
Institución:Universidad Privada Norbert Wiener
Repositorio:UWIENER-Institucional
Lenguaje:inglés
OAI Identifier:oai:repositorio.uwiener.edu.pe:20.500.13053/8626
Enlace del recurso:https://hdl.handle.net/20.500.13053/8626
https://doi.org/10.3390/vaccines11020312
Nivel de acceso:acceso abierto
Materia:monkeypox; sentiment; tweets; CNN; LSTM
3.00.00 -- Ciencias médicas, Ciencias de la salud
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dc.title.es_PE.fl_str_mv “The Public Health Contribution of Sentiment Analysis of Monkeypox Tweets to Detect Polarities Using the CNN-LSTM Model“
title “The Public Health Contribution of Sentiment Analysis of Monkeypox Tweets to Detect Polarities Using the CNN-LSTM Model“
spellingShingle “The Public Health Contribution of Sentiment Analysis of Monkeypox Tweets to Detect Polarities Using the CNN-LSTM Model“
Iparraguirre-Villanueva, Orlando
monkeypox; sentiment; tweets; CNN; LSTM
3.00.00 -- Ciencias médicas, Ciencias de la salud
title_short “The Public Health Contribution of Sentiment Analysis of Monkeypox Tweets to Detect Polarities Using the CNN-LSTM Model“
title_full “The Public Health Contribution of Sentiment Analysis of Monkeypox Tweets to Detect Polarities Using the CNN-LSTM Model“
title_fullStr “The Public Health Contribution of Sentiment Analysis of Monkeypox Tweets to Detect Polarities Using the CNN-LSTM Model“
title_full_unstemmed “The Public Health Contribution of Sentiment Analysis of Monkeypox Tweets to Detect Polarities Using the CNN-LSTM Model“
title_sort “The Public Health Contribution of Sentiment Analysis of Monkeypox Tweets to Detect Polarities Using the CNN-LSTM Model“
author Iparraguirre-Villanueva, Orlando
author_facet Iparraguirre-Villanueva, Orlando
Alvarez-Risco, Aldo
Herrera Salazar, Jose Luis
Beltozar-Clemente, Saul
Zapata-Paulini, Joselyn
Yáñez, Jaime A.
Cabanillas-Carbonell, Michael
author_role author
author2 Alvarez-Risco, Aldo
Herrera Salazar, Jose Luis
Beltozar-Clemente, Saul
Zapata-Paulini, Joselyn
Yáñez, Jaime A.
Cabanillas-Carbonell, Michael
author2_role author
author
author
author
author
author
dc.contributor.author.fl_str_mv Iparraguirre-Villanueva, Orlando
Alvarez-Risco, Aldo
Herrera Salazar, Jose Luis
Beltozar-Clemente, Saul
Zapata-Paulini, Joselyn
Yáñez, Jaime A.
Cabanillas-Carbonell, Michael
dc.subject.es_PE.fl_str_mv monkeypox; sentiment; tweets; CNN; LSTM
topic monkeypox; sentiment; tweets; CNN; LSTM
3.00.00 -- Ciencias médicas, Ciencias de la salud
dc.subject.ocde.es_PE.fl_str_mv 3.00.00 -- Ciencias médicas, Ciencias de la salud
description “Monkeypox is a rare disease caused by the monkeypox virus. This disease was considered eradicated in 1980 and was believed to affect rodents and not humans. However, recent years have seen a massive outbreak of monkeypox in humans, setting off worldwide alerts from health agencies. As of September 2022, the number of confirmed cases in Peru had reached 1964. Although most monkeypox patients have been discharged, we cannot neglect the monitoring of the population with respect to the monkeypox virus. Lately, the population has started to express their feelings and opinions through social media, specifically Twitter, as it is the most used social medium and is an ideal space to gather what people think about the monkeypox virus. The information imparted through this medium can be in different formats, such as text, videos, images, audio, etc. The objective of this work is to analyze the positive, negative, and neutral feelings of people who publish their opinions on Twitter with the hashtag #Monkeypox. To find out what people think about this disease, a hybrid-based model architecture built on CNN and LSTM was used to determine the prediction accuracy. The prediction result obtained from the total monkeypox data was 83% accurate. Other performance metrics were also used to evaluate the model, such as specificity, recall level, and F1 score, representing 99%, 85%, and 88%, respectively. The results also showed the polarity of feelings through the CNN-LSTM confusion matrix, where 45.42% of people expressed neither positive nor negative opinions, while 19.45% expressed negative and fearful feelings about this infectious disease. The results of this work contribute to raising public awareness about the monkeypox virus. “
publishDate 2023
dc.date.accessioned.none.fl_str_mv 2023-05-26T17:05:16Z
dc.date.available.none.fl_str_mv 2023-05-26T17:05:16Z
dc.date.issued.fl_str_mv 2023-01-31
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dc.identifier.doi.none.fl_str_mv https://doi.org/10.3390/vaccines11020312
url https://hdl.handle.net/20.500.13053/8626
https://doi.org/10.3390/vaccines11020312
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spelling Iparraguirre-Villanueva, OrlandoAlvarez-Risco, AldoHerrera Salazar, Jose LuisBeltozar-Clemente, SaulZapata-Paulini, JoselynYáñez, Jaime A.Cabanillas-Carbonell, Michael2023-05-26T17:05:16Z2023-05-26T17:05:16Z2023-01-31https://hdl.handle.net/20.500.13053/8626https://doi.org/10.3390/vaccines11020312“Monkeypox is a rare disease caused by the monkeypox virus. This disease was considered eradicated in 1980 and was believed to affect rodents and not humans. However, recent years have seen a massive outbreak of monkeypox in humans, setting off worldwide alerts from health agencies. As of September 2022, the number of confirmed cases in Peru had reached 1964. Although most monkeypox patients have been discharged, we cannot neglect the monitoring of the population with respect to the monkeypox virus. Lately, the population has started to express their feelings and opinions through social media, specifically Twitter, as it is the most used social medium and is an ideal space to gather what people think about the monkeypox virus. The information imparted through this medium can be in different formats, such as text, videos, images, audio, etc. The objective of this work is to analyze the positive, negative, and neutral feelings of people who publish their opinions on Twitter with the hashtag #Monkeypox. To find out what people think about this disease, a hybrid-based model architecture built on CNN and LSTM was used to determine the prediction accuracy. The prediction result obtained from the total monkeypox data was 83% accurate. Other performance metrics were also used to evaluate the model, such as specificity, recall level, and F1 score, representing 99%, 85%, and 88%, respectively. The results also showed the polarity of feelings through the CNN-LSTM confusion matrix, where 45.42% of people expressed neither positive nor negative opinions, while 19.45% expressed negative and fearful feelings about this infectious disease. The results of this work contribute to raising public awareness about the monkeypox virus. “application/pdfengMDPIUKinfo:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by/4.0/monkeypox; sentiment; tweets; CNN; LSTM3.00.00 -- Ciencias médicas, Ciencias de la salud“The Public Health Contribution of Sentiment Analysis of Monkeypox Tweets to Detect Polarities Using the CNN-LSTM Model“info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionreponame:UWIENER-Institucionalinstname:Universidad Privada Norbert Wienerinstacron:UWIENERPublicationTEXTvaccines-11-00312.pdf.txtvaccines-11-00312.pdf.txtExtracted texttext/plain52357https://dspace-uwiener.metabuscador.org/bitstreams/8d0fb4fc-ab13-4db8-9172-78844d3b29c7/downloadb6c83fdf0e2b1f544dfbacb555095c02MD53THUMBNAILvaccines-11-00312.pdf.jpgvaccines-11-00312.pdf.jpgGenerated Thumbnailimage/jpeg12373https://dspace-uwiener.metabuscador.org/bitstreams/51793e3c-06c6-40d3-89f2-c2f14bffe8a9/download11ba0f7c4c02f0b67648bfcf89e2fe97MD54ORIGINALvaccines-11-00312.pdfvaccines-11-00312.pdfapplication/pdf2060192https://dspace-uwiener.metabuscador.org/bitstreams/954a41a7-345a-495f-9d8a-1d843386cb98/download79e7491fab347ba4a1a22dbac1658c88MD51LICENSElicense.txtlicense.txttext/plain; charset=utf-81748https://dspace-uwiener.metabuscador.org/bitstreams/b15aa700-0e60-4102-ba96-85bcdeb73064/download8a4605be74aa9ea9d79846c1fba20a33MD5220.500.13053/8626oai:dspace-uwiener.metabuscador.org:20.500.13053/86262024-12-13 14:23:33.086https://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessopen.accesshttps://dspace-uwiener.metabuscador.orgRepositorio Institucional de la Universidad de Wienerbdigital@metabiblioteca.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