Comparison of Support Vector Machine and XGBSVM in Analyzing Public Opinion on Covid-19 Vaccination

Rahmaddeni Rahmaddeni, M. K. Anam, Yuda Irawan, S. Susanti, M. Jamaris
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引用次数: 3

Abstract

The coronavirus has become a global pandemic and has spread almost all over the world, including Indonesia. The spread of COVID-19 in Indonesia causes many negative impacts. Therefore, the government took vaccination measures to suppress the spread of COVID-19. The public's response to vaccination was quite diverse on Twitter, some were supportive, and some were not. The data used in this study came from Twitter which was taken using the emprit drone portal by using the keyword, "vaccination." The classification is conducted using the SVM and hybrid methods between SVM and XGBoost or what is commonly called XGBSVM. The purpose of this study is to provide an overview to the public on whether the Covid-19 vaccination tends to create positive, neutral, or negative opinions. The results of the sentiment evaluation show that SVM has
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支持向量机与XGBSVM在Covid-19疫苗接种民意分析中的比较
冠状病毒已成为全球大流行,几乎蔓延到包括印度尼西亚在内的世界各地。新冠肺炎疫情在印尼的蔓延造成了诸多负面影响。因此,政府采取了疫苗接种措施来抑制COVID-19的传播。在推特上,公众对疫苗接种的反应相当多样化,有些人支持,有些人不支持。本研究中使用的数据来自Twitter,该数据是通过emprit无人机门户网站使用关键词“疫苗接种”获取的。使用SVM和SVM与XGBoost的混合方法进行分类,也就是通常所说的XGBSVM。本研究的目的是向公众提供关于Covid-19疫苗接种是否倾向于产生积极,中立或消极意见的概述。情感评价结果表明,支持向量机具有良好的性能
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