Unmasking of Heart Disease Symptoms Using the COVID-19 Vaccine Dataset in Twitter

Author:

Shyamala Devi N.1ORCID,Sharmila K.1,Grace Hannah J.1ORCID

Affiliation:

1. Vels Institute of Science, Technology, and Advanced Studies, India

Abstract

The chapter delves into the intricate web of conversations surrounding the COVID-19 vaccine on Twitter and explores its potential association with heart disease symptoms. In an era where social media plays a pivotal role in shaping public perception and disseminating information, understanding the narratives and concerns around vaccine safety is of paramount importance. Leveraging a dataset curated from Twitter discussions, the authors employ natural language processing techniques and sentiment analysis to unearth insights regarding heart disease symptoms mentioned in the context of COVID-19 vaccination. This research unearths the sentiments, trends, and possible correlations within this corpus of Twitter data. By unmasking potential connections between COVID-19 vaccination and heart disease symptoms, this study contributes to a more comprehensive understanding of vaccine-related discussions and their implications for public health.

Publisher

IGI Global

Reference14 articles.

1. Drug disease relation extraction from biomedical literature using NLP and machine learning.;W.Ben Abdessalem Karaa;Mobile Information Systems,2021

2. Exploring Biosignals for Quantitative Pain Assessment in Cancer Patients: A Proof of Concept.;M.Cascella;Electronics (Basel),2023

3. Herbal medicine used in the treatment of human diseases in the Rif, Northern Morocco.;N.Chaachouay;Arabian Journal for Science and Engineering,2022

4. Evaluating natural language processing applications applied to outbreak and disease surveillance.;W. W.Chapman;Proceedings of 36th symposium on the interface: computing science and statistics,2004

5. SARS–CoV‐2 infection and COVID‐19 outcomes in rheumatic diseases: A systematic literature review and meta‐analysis.;R.Conway;Arthritis & Rheumatology (Hoboken, N.J.),2022

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3