Sentiment Analysis of the Covid-19 Vaccines on Social Media

Chad A. Melton; Olufunto A. Olusanya; Nariman Ammar; Arash Shaban-Nejad

doi:10.3233/SHTI220265

Sentiment Analysis of the Covid-19 Vaccines on Social Media

Chad A. Melton
, Olufunto A. Olusanya
, Nariman Ammar
, Arash Shaban-Nejad

Human Geography

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

2 Scopus citations

Abstract

The COVID-19 pandemic fueled one of the quickest vaccine developments in history. Misinformation on online social media often leads to negative vaccine sentiment. We conducted a sentiment analysis and Latent Dirichlet Allocation topic modeling from Reddit communities focusing on the COVID-19 vaccine. Polarity analysis suggested these communities expressed positive sentiment regarding the vaccine. However, topic modeling revealed community members mainly focused on the side effects and vaccination experience.

Original language	English
Title of host publication	MEDINFO 2021
Subtitle of host publication	One World, One Health - Global Partnership for Digital Innovation - Proceedings of the 18th World Congress on Medical and Health Informatics
Editors	Paula Otero, Philip Scott, Susan Z. Martin, Elaine Huesing
Publisher	IOS Press BV
Pages	1056-1057
Number of pages	2
ISBN (Electronic)	9781643682648
DOIs	https://doi.org/10.3233/SHTI220265
State	Published - Jun 6 2022
Event	18th World Congress on Medical and Health Informatics: One World, One Health - Global Partnership for Digital Innovation, MEDINFO 2021 - Virtual, Online Duration: Oct 2 2021 → Oct 4 2021

Publication series

Name	Studies in Health Technology and Informatics
Volume	290
ISSN (Print)	0926-9630
ISSN (Electronic)	1879-8365

Conference

Conference	18th World Congress on Medical and Health Informatics: One World, One Health - Global Partnership for Digital Innovation, MEDINFO 2021
City	Virtual, Online
Period	10/2/21 → 10/4/21

Keywords

Covid-19
Natural Language Processing
Vaccines

Access to Document

10.3233/SHTI220265

Cite this

Melton, C. A., Olusanya, O. A., Ammar, N., & Shaban-Nejad, A. (2022). Sentiment Analysis of the Covid-19 Vaccines on Social Media. In P. Otero, P. Scott, S. Z. Martin, & E. Huesing (Eds.), MEDINFO 2021: One World, One Health - Global Partnership for Digital Innovation - Proceedings of the 18th World Congress on Medical and Health Informatics (pp. 1056-1057). (Studies in Health Technology and Informatics; Vol. 290). IOS Press BV. https://doi.org/10.3233/SHTI220265

Melton, Chad A. ; Olusanya, Olufunto A. ; Ammar, Nariman et al. / Sentiment Analysis of the Covid-19 Vaccines on Social Media. MEDINFO 2021: One World, One Health - Global Partnership for Digital Innovation - Proceedings of the 18th World Congress on Medical and Health Informatics. editor / Paula Otero ; Philip Scott ; Susan Z. Martin ; Elaine Huesing. IOS Press BV, 2022. pp. 1056-1057 (Studies in Health Technology and Informatics).

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title = "Sentiment Analysis of the Covid-19 Vaccines on Social Media",

abstract = "The COVID-19 pandemic fueled one of the quickest vaccine developments in history. Misinformation on online social media often leads to negative vaccine sentiment. We conducted a sentiment analysis and Latent Dirichlet Allocation topic modeling from Reddit communities focusing on the COVID-19 vaccine. Polarity analysis suggested these communities expressed positive sentiment regarding the vaccine. However, topic modeling revealed community members mainly focused on the side effects and vaccination experience.",

keywords = "Covid-19, Natural Language Processing, Vaccines",

author = "Melton, \{Chad A.\} and Olusanya, \{Olufunto A.\} and Nariman Ammar and Arash Shaban-Nejad",

note = "Publisher Copyright: {\textcopyright} 2022 International Medical Informatics Association (IMIA) and IOS Press.; 18th World Congress on Medical and Health Informatics: One World, One Health - Global Partnership for Digital Innovation, MEDINFO 2021 ; Conference date: 02-10-2021 Through 04-10-2021",

year = "2022",

month = jun,

day = "6",

doi = "10.3233/SHTI220265",

language = "English",

series = "Studies in Health Technology and Informatics",

publisher = "IOS Press BV",

pages = "1056--1057",

editor = "Paula Otero and Philip Scott and Martin, \{Susan Z.\} and Elaine Huesing",

booktitle = "MEDINFO 2021",

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Melton, CA, Olusanya, OA, Ammar, N & Shaban-Nejad, A 2022, Sentiment Analysis of the Covid-19 Vaccines on Social Media. in P Otero, P Scott, SZ Martin & E Huesing (eds), MEDINFO 2021: One World, One Health - Global Partnership for Digital Innovation - Proceedings of the 18th World Congress on Medical and Health Informatics. Studies in Health Technology and Informatics, vol. 290, IOS Press BV, pp. 1056-1057, 18th World Congress on Medical and Health Informatics: One World, One Health - Global Partnership for Digital Innovation, MEDINFO 2021, Virtual, Online, 10/2/21. https://doi.org/10.3233/SHTI220265

Sentiment Analysis of the Covid-19 Vaccines on Social Media. / Melton, Chad A.; Olusanya, Olufunto A.; Ammar, Nariman et al.
MEDINFO 2021: One World, One Health - Global Partnership for Digital Innovation - Proceedings of the 18th World Congress on Medical and Health Informatics. ed. / Paula Otero; Philip Scott; Susan Z. Martin; Elaine Huesing. IOS Press BV, 2022. p. 1056-1057 (Studies in Health Technology and Informatics; Vol. 290).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Sentiment Analysis of the Covid-19 Vaccines on Social Media

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AU - Olusanya, Olufunto A.

AU - Ammar, Nariman

AU - Shaban-Nejad, Arash

PY - 2022/6/6

Y1 - 2022/6/6

N2 - The COVID-19 pandemic fueled one of the quickest vaccine developments in history. Misinformation on online social media often leads to negative vaccine sentiment. We conducted a sentiment analysis and Latent Dirichlet Allocation topic modeling from Reddit communities focusing on the COVID-19 vaccine. Polarity analysis suggested these communities expressed positive sentiment regarding the vaccine. However, topic modeling revealed community members mainly focused on the side effects and vaccination experience.

AB - The COVID-19 pandemic fueled one of the quickest vaccine developments in history. Misinformation on online social media often leads to negative vaccine sentiment. We conducted a sentiment analysis and Latent Dirichlet Allocation topic modeling from Reddit communities focusing on the COVID-19 vaccine. Polarity analysis suggested these communities expressed positive sentiment regarding the vaccine. However, topic modeling revealed community members mainly focused on the side effects and vaccination experience.

KW - Covid-19

KW - Natural Language Processing

KW - Vaccines

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DO - 10.3233/SHTI220265

M3 - Conference contribution

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AN - SCOPUS:85131468701

T3 - Studies in Health Technology and Informatics

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BT - MEDINFO 2021

A2 - Otero, Paula

A2 - Scott, Philip

A2 - Martin, Susan Z.

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Y2 - 2 October 2021 through 4 October 2021

ER -

Melton CA, Olusanya OA, Ammar N, Shaban-Nejad A. Sentiment Analysis of the Covid-19 Vaccines on Social Media. In Otero P, Scott P, Martin SZ, Huesing E, editors, MEDINFO 2021: One World, One Health - Global Partnership for Digital Innovation - Proceedings of the 18th World Congress on Medical and Health Informatics. IOS Press BV. 2022. p. 1056-1057. (Studies in Health Technology and Informatics). doi: 10.3233/SHTI220265

Sentiment Analysis of the Covid-19 Vaccines on Social Media

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