Creating a Metamodel Based on Machine Learning to Identify the Sentiment of Vaccine and Disease-Related Messages in Twitter: the MAVIS Study

A. R. González, J. Tuñas, Diego Fernandez Peces-Barba, Ernestina Menasalvas Ruiz, A. Jaramillo, M. Cotarelo, Antonio Conejo, Amalia Arce, A. Gil
{"title":"Creating a Metamodel Based on Machine Learning to Identify the Sentiment of Vaccine and Disease-Related Messages in Twitter: the MAVIS Study","authors":"A. R. González, J. Tuñas, Diego Fernandez Peces-Barba, Ernestina Menasalvas Ruiz, A. Jaramillo, M. Cotarelo, Antonio Conejo, Amalia Arce, A. Gil","doi":"10.1109/CBMS49503.2020.00053","DOIUrl":null,"url":null,"abstract":"MAVIS was a project that aimed to study the interactions in social networks (Twitter and Instagram) between users regarding the sentiment expressed in their messages when they talked about specific vaccines or diseases. The study was performed during the period 2015-2018 and was initially technically done by using a set of commercial tools to identify the polarity of the messages. With the aim of improving the results provided by such tools, we performed a deep analysis of the results from such tools and provide a machine learning method as a metamodel over the results of the commercial tools. In this paper we explain both the technical process performed together with the main results that were obtained.","PeriodicalId":121059,"journal":{"name":"2020 IEEE 33rd International Symposium on Computer-Based Medical Systems (CBMS)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 IEEE 33rd International Symposium on Computer-Based Medical Systems (CBMS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CBMS49503.2020.00053","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2

Abstract

MAVIS was a project that aimed to study the interactions in social networks (Twitter and Instagram) between users regarding the sentiment expressed in their messages when they talked about specific vaccines or diseases. The study was performed during the period 2015-2018 and was initially technically done by using a set of commercial tools to identify the polarity of the messages. With the aim of improving the results provided by such tools, we performed a deep analysis of the results from such tools and provide a machine learning method as a metamodel over the results of the commercial tools. In this paper we explain both the technical process performed together with the main results that were obtained.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
创建一个基于机器学习的元模型来识别Twitter中疫苗和疾病相关信息的情绪:MAVIS研究
MAVIS是一个项目,旨在研究用户在社交网络(Twitter和Instagram)上谈论特定疫苗或疾病时,他们在信息中表达的情绪。该研究在2015-2018年期间进行,最初是通过使用一套商业工具来识别消息的极性来完成的。为了改进这些工具提供的结果,我们对这些工具的结果进行了深入分析,并提供了一种机器学习方法,作为商业工具结果的元模型。在本文中,我们解释了所执行的技术过程以及所获得的主要结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
A Virtual Assistant for Cybersickness Care Image Classification of Cyanobacteria Microcystis Aeruginosa in raw Water Samples in Curitiba's Region Enrich Rare Disease Phenotypic Characterizations via a Graph Convolutional Network Based Recommendation System A Clustering Framework for Patient Phenotyping with Application to Adverse Drug Events DermaDL: Advanced Convolutional Neural Networks for Automated Melanoma Detection
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1