Creating a metamodel based on machine learning to identify the sentiment of vaccine and disease-related messages in Twitter: the MAVIS study

被引：0
|
作者：
Rodriguez-Gonzalez, Alejandro [1 ,2 ]
Manuel Tunas, Juan [2 ]
Fernandez Peces-Barba, Diego [2 ]
Menasalvas-Ruiz, Ernestina [1 ,2 ]
Jaramillo, Almudena [3 ]
Cotarelo, Manuel [3 ]
Conejo, Antonio [4 ]
Arce, Amalia [5 ]
Gil, Angel [5 ]
机构：
[1] Univ Politecn Madrid, Ctr Tecnol Biomed, Madrid, Spain
[2] Univ Politecn Madrid, ETS Ingenieros Informat, Madrid, Spain
[3] MSD Espana, Madrid, Spain
[4] Hosp Vithas Xanit Int, Malaga, Spain
[5] Fundacio Hosp Nens, Barcelona, Spain
来源：
2020 IEEE 33RD INTERNATIONAL SYMPOSIUM ON COMPUTER-BASED MEDICAL SYSTEMS(CBMS 2020) | 2020年
关键词：
twitter; vaccine; machine learning; sentiment analysis; metamodel; FACEBOOK; INFORMATION; INTERNET;
D O I：
10.1109/CBMS49503.2020.00053
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
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.
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收藏
页码：245 / 250
页数：6
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