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Investigating Public Sentiment on Laryngeal Cancer in 2022 Using Machine Learning

This study aims to investigate public sentiment on laryngeal cancer via tweets in 2022 using machine learning. We aimed to analyze the public sentiment about laryngeal cancer on Twitter last year. A novel dataset was created for the purpose of this study by scraping all tweets from 1st Jan 2022 that...

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Autores principales: Rao, Divya, Singh, Rohit, Prakashini, K., Vijayananda, J.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer India 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10132422/
https://www.ncbi.nlm.nih.gov/pubmed/37362133
http://dx.doi.org/10.1007/s12070-023-03813-2
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author Rao, Divya
Singh, Rohit
Prakashini, K.
Vijayananda, J.
author_facet Rao, Divya
Singh, Rohit
Prakashini, K.
Vijayananda, J.
author_sort Rao, Divya
collection PubMed
description This study aims to investigate public sentiment on laryngeal cancer via tweets in 2022 using machine learning. We aimed to analyze the public sentiment about laryngeal cancer on Twitter last year. A novel dataset was created for the purpose of this study by scraping all tweets from 1st Jan 2022 that included the hashtags #throatcancer, #laryngealcancer, #supraglotticcancer, #glotticcancer, and #subglotticcancer in their text. After all tweets underwent a fourfold data cleaning process, they were analyzed using natural language processing and sentiment analysis techniques to classify tweets into positive, negative, or neutral categories and to identify common themes and topics related to laryngeal cancer. The study analyzed a corpus of 733 tweets related to laryngeal cancer. The sentiment analysis revealed that 53% of the tweets were neutral, 34% were positive, and 13% were negative. The most common themes identified in the tweets were treatment and therapy, risk factors, symptoms and diagnosis, prevention and awareness, and emotional impact. This study highlights the potential of social media platforms like Twitter as a valuable source of real-time, patient-generated data that can inform healthcare research and practice. Our findings suggest that while Twitter is a popular platform, the limited number of tweets related to laryngeal cancer indicates that a better strategy could be developed for online communication among netizens regarding the awareness of laryngeal cancer.
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spelling pubmed-101324222023-04-27 Investigating Public Sentiment on Laryngeal Cancer in 2022 Using Machine Learning Rao, Divya Singh, Rohit Prakashini, K. Vijayananda, J. Indian J Otolaryngol Head Neck Surg Original Article This study aims to investigate public sentiment on laryngeal cancer via tweets in 2022 using machine learning. We aimed to analyze the public sentiment about laryngeal cancer on Twitter last year. A novel dataset was created for the purpose of this study by scraping all tweets from 1st Jan 2022 that included the hashtags #throatcancer, #laryngealcancer, #supraglotticcancer, #glotticcancer, and #subglotticcancer in their text. After all tweets underwent a fourfold data cleaning process, they were analyzed using natural language processing and sentiment analysis techniques to classify tweets into positive, negative, or neutral categories and to identify common themes and topics related to laryngeal cancer. The study analyzed a corpus of 733 tweets related to laryngeal cancer. The sentiment analysis revealed that 53% of the tweets were neutral, 34% were positive, and 13% were negative. The most common themes identified in the tweets were treatment and therapy, risk factors, symptoms and diagnosis, prevention and awareness, and emotional impact. This study highlights the potential of social media platforms like Twitter as a valuable source of real-time, patient-generated data that can inform healthcare research and practice. Our findings suggest that while Twitter is a popular platform, the limited number of tweets related to laryngeal cancer indicates that a better strategy could be developed for online communication among netizens regarding the awareness of laryngeal cancer. Springer India 2023-04-26 2023-09 /pmc/articles/PMC10132422/ /pubmed/37362133 http://dx.doi.org/10.1007/s12070-023-03813-2 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Original Article
Rao, Divya
Singh, Rohit
Prakashini, K.
Vijayananda, J.
Investigating Public Sentiment on Laryngeal Cancer in 2022 Using Machine Learning
title Investigating Public Sentiment on Laryngeal Cancer in 2022 Using Machine Learning
title_full Investigating Public Sentiment on Laryngeal Cancer in 2022 Using Machine Learning
title_fullStr Investigating Public Sentiment on Laryngeal Cancer in 2022 Using Machine Learning
title_full_unstemmed Investigating Public Sentiment on Laryngeal Cancer in 2022 Using Machine Learning
title_short Investigating Public Sentiment on Laryngeal Cancer in 2022 Using Machine Learning
title_sort investigating public sentiment on laryngeal cancer in 2022 using machine learning
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10132422/
https://www.ncbi.nlm.nih.gov/pubmed/37362133
http://dx.doi.org/10.1007/s12070-023-03813-2
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