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Early-stage pregnancy recognition on microblogs: Machine learning and lexicon-based approaches

Pregnancy carries high medical and psychosocial risks that could lead pregnant women to experience serious health consequences. Providing protective measures for pregnant women is one of the critical tasks during the pregnancy period. This study proposes an emotion-based mechanism to detect the earl...

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Autores principales: Sarsam, Samer Muthana, Alzahrani, Ahmed Ibrahim, Al-Samarraie, Hosam
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10559919/
https://www.ncbi.nlm.nih.gov/pubmed/37809524
http://dx.doi.org/10.1016/j.heliyon.2023.e20132
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author Sarsam, Samer Muthana
Alzahrani, Ahmed Ibrahim
Al-Samarraie, Hosam
author_facet Sarsam, Samer Muthana
Alzahrani, Ahmed Ibrahim
Al-Samarraie, Hosam
author_sort Sarsam, Samer Muthana
collection PubMed
description Pregnancy carries high medical and psychosocial risks that could lead pregnant women to experience serious health consequences. Providing protective measures for pregnant women is one of the critical tasks during the pregnancy period. This study proposes an emotion-based mechanism to detect the early stage of pregnancy using real-time data from Twitter. Pregnancy-related emotions (e.g., anger, fear, sadness, joy, and surprise) and polarity (positive and negative) were extracted from users' tweets using NRC Affect Intensity Lexicon and SentiStrength techniques. Then, pregnancy-related terms were extracted and mapped with pregnancy-related sentiments using part-of-speech tagging and association rules mining techniques. The results showed that pregnancy tweets contained high positivity, as well as significant amounts of joy, sadness, and fear. The classification results demonstrated the possibility of using users’ sentiments for early-stage pregnancy recognition on microblogs. The proposed mechanism offers valuable insights to healthcare decision-makers, allowing them to develop a comprehensive understanding of users' health status based on social media posts.
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spelling pubmed-105599192023-10-08 Early-stage pregnancy recognition on microblogs: Machine learning and lexicon-based approaches Sarsam, Samer Muthana Alzahrani, Ahmed Ibrahim Al-Samarraie, Hosam Heliyon Research Article Pregnancy carries high medical and psychosocial risks that could lead pregnant women to experience serious health consequences. Providing protective measures for pregnant women is one of the critical tasks during the pregnancy period. This study proposes an emotion-based mechanism to detect the early stage of pregnancy using real-time data from Twitter. Pregnancy-related emotions (e.g., anger, fear, sadness, joy, and surprise) and polarity (positive and negative) were extracted from users' tweets using NRC Affect Intensity Lexicon and SentiStrength techniques. Then, pregnancy-related terms were extracted and mapped with pregnancy-related sentiments using part-of-speech tagging and association rules mining techniques. The results showed that pregnancy tweets contained high positivity, as well as significant amounts of joy, sadness, and fear. The classification results demonstrated the possibility of using users’ sentiments for early-stage pregnancy recognition on microblogs. The proposed mechanism offers valuable insights to healthcare decision-makers, allowing them to develop a comprehensive understanding of users' health status based on social media posts. Elsevier 2023-09-14 /pmc/articles/PMC10559919/ /pubmed/37809524 http://dx.doi.org/10.1016/j.heliyon.2023.e20132 Text en © 2023 The Authors https://creativecommons.org/licenses/by/4.0/This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Research Article
Sarsam, Samer Muthana
Alzahrani, Ahmed Ibrahim
Al-Samarraie, Hosam
Early-stage pregnancy recognition on microblogs: Machine learning and lexicon-based approaches
title Early-stage pregnancy recognition on microblogs: Machine learning and lexicon-based approaches
title_full Early-stage pregnancy recognition on microblogs: Machine learning and lexicon-based approaches
title_fullStr Early-stage pregnancy recognition on microblogs: Machine learning and lexicon-based approaches
title_full_unstemmed Early-stage pregnancy recognition on microblogs: Machine learning and lexicon-based approaches
title_short Early-stage pregnancy recognition on microblogs: Machine learning and lexicon-based approaches
title_sort early-stage pregnancy recognition on microblogs: machine learning and lexicon-based approaches
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10559919/
https://www.ncbi.nlm.nih.gov/pubmed/37809524
http://dx.doi.org/10.1016/j.heliyon.2023.e20132
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