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Leveraging machine learning-based approaches to assess human papillomavirus vaccination sentiment trends with Twitter data
BACKGROUND: As one of the serious public health issues, vaccination refusal has been attracting more and more attention, especially for newly approved human papillomavirus (HPV) vaccines. Understanding public opinion towards HPV vaccines, especially concerns on social media, is of significant import...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5506590/ https://www.ncbi.nlm.nih.gov/pubmed/28699569 http://dx.doi.org/10.1186/s12911-017-0469-6 |
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author | Du, Jingcheng Xu, Jun Song, Hsing-Yi Tao, Cui |
author_facet | Du, Jingcheng Xu, Jun Song, Hsing-Yi Tao, Cui |
author_sort | Du, Jingcheng |
collection | PubMed |
description | BACKGROUND: As one of the serious public health issues, vaccination refusal has been attracting more and more attention, especially for newly approved human papillomavirus (HPV) vaccines. Understanding public opinion towards HPV vaccines, especially concerns on social media, is of significant importance for HPV vaccination promotion. METHODS: In this study, we leveraged a hierarchical machine learning based sentiment analysis system to extract public opinions towards HPV vaccines from Twitter. English tweets containing HPV vaccines-related keywords were collected from November 2, 2015 to March 28, 2016. Manual annotation was done to evaluate the performance of the system on the unannotated tweets corpus. Followed time series analysis was applied to this corpus to track the trends of machine-deduced sentiments and their associations with different days of the week. RESULTS: The evaluation of the unannotated tweets corpus showed that the micro-averaging F scores have reached 0.786. The learning system deduced the sentiment labels for 184,214 tweets in the collected unannotated tweets corpus. Time series analysis identified a coincidence between mainstream outcome and Twitter contents. A weak trend was found for “Negative” tweets that decreased firstly and began to increase later; an opposite trend was identified for “Positive” tweets. Tweets that contain the worries on efficacy for HPV vaccines showed a relative significant decreasing trend. Strong associations were found between some sentiments (“Positive”, “Negative”, “Negative-Safety” and “Negative-Others”) with different days of the week. CONCLUSIONS: Our efforts on sentiment analysis for newly approved HPV vaccines provide us an automatic and instant way to extract public opinion and understand the concerns on Twitter. Our approaches can provide a feedback to public health professionals to monitor online public response, examine the effectiveness of their HPV vaccination promotion strategies and adjust their promotion plans. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12911-017-0469-6) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-5506590 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-55065902017-07-12 Leveraging machine learning-based approaches to assess human papillomavirus vaccination sentiment trends with Twitter data Du, Jingcheng Xu, Jun Song, Hsing-Yi Tao, Cui BMC Med Inform Decis Mak Research BACKGROUND: As one of the serious public health issues, vaccination refusal has been attracting more and more attention, especially for newly approved human papillomavirus (HPV) vaccines. Understanding public opinion towards HPV vaccines, especially concerns on social media, is of significant importance for HPV vaccination promotion. METHODS: In this study, we leveraged a hierarchical machine learning based sentiment analysis system to extract public opinions towards HPV vaccines from Twitter. English tweets containing HPV vaccines-related keywords were collected from November 2, 2015 to March 28, 2016. Manual annotation was done to evaluate the performance of the system on the unannotated tweets corpus. Followed time series analysis was applied to this corpus to track the trends of machine-deduced sentiments and their associations with different days of the week. RESULTS: The evaluation of the unannotated tweets corpus showed that the micro-averaging F scores have reached 0.786. The learning system deduced the sentiment labels for 184,214 tweets in the collected unannotated tweets corpus. Time series analysis identified a coincidence between mainstream outcome and Twitter contents. A weak trend was found for “Negative” tweets that decreased firstly and began to increase later; an opposite trend was identified for “Positive” tweets. Tweets that contain the worries on efficacy for HPV vaccines showed a relative significant decreasing trend. Strong associations were found between some sentiments (“Positive”, “Negative”, “Negative-Safety” and “Negative-Others”) with different days of the week. CONCLUSIONS: Our efforts on sentiment analysis for newly approved HPV vaccines provide us an automatic and instant way to extract public opinion and understand the concerns on Twitter. Our approaches can provide a feedback to public health professionals to monitor online public response, examine the effectiveness of their HPV vaccination promotion strategies and adjust their promotion plans. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12911-017-0469-6) contains supplementary material, which is available to authorized users. BioMed Central 2017-07-05 /pmc/articles/PMC5506590/ /pubmed/28699569 http://dx.doi.org/10.1186/s12911-017-0469-6 Text en © The Author(s). 2017 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Research Du, Jingcheng Xu, Jun Song, Hsing-Yi Tao, Cui Leveraging machine learning-based approaches to assess human papillomavirus vaccination sentiment trends with Twitter data |
title | Leveraging machine learning-based approaches to assess human papillomavirus vaccination sentiment trends with Twitter data |
title_full | Leveraging machine learning-based approaches to assess human papillomavirus vaccination sentiment trends with Twitter data |
title_fullStr | Leveraging machine learning-based approaches to assess human papillomavirus vaccination sentiment trends with Twitter data |
title_full_unstemmed | Leveraging machine learning-based approaches to assess human papillomavirus vaccination sentiment trends with Twitter data |
title_short | Leveraging machine learning-based approaches to assess human papillomavirus vaccination sentiment trends with Twitter data |
title_sort | leveraging machine learning-based approaches to assess human papillomavirus vaccination sentiment trends with twitter data |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5506590/ https://www.ncbi.nlm.nih.gov/pubmed/28699569 http://dx.doi.org/10.1186/s12911-017-0469-6 |
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