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

González, Alejandro Rodríguez; Tuñas, Juan Manuel; Peces-Barba, Diego Fernández; Ruiz, Ernestina Menasalvas; Jaramillo, Almudena; Cotarelo, Manuel; Conejo, Antonio; Arce, Amalia; Gil, Á.

doi:10.1109/cbms49503.2020.00053

Cited by 2 publications

(3 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Although a manual validation of the three systems independently showed a barely reasonable accuracy, the reality is that none of the systems separately provides sufficiently accuracy to consider the use of the system to identify the sentiment expressed. The results from 43 showed that the use of commercial tools independently did not provide enough accuracy in the identification of the tweets individually. The agreement level between both tools and evaluators was quite uneven, as it can be seen in.…”

Section: General Methodologymentioning

confidence: 98%

“…For this reason, the paper will not dig into the technical details about the tweet sentiment identification, which has been already described with anteriority. 43 …”

Section: General Methodologymentioning

confidence: 99%

“…As has been described, the metamodel (created by a Random Forests classifier) allows to identify the sentiment expressed with a ROC value of 0.86. 43 Such ROC value ascended to 0.9 when generating the model by up-sampling the under-represented negative tweets set. 51 Further comparisons with other ML techniques (MultiLayer Perceptron, C5.0, Logit Boost, Bayes and Support Vector Machine methods) to develop this metamodel can be explored in the previous work.…”

Section: General Methodologymentioning

confidence: 99%

See 2 more Smart Citations

Influenza and Measles-MMR: two case study of the trend and impact of vaccine-related Twitter posts in Spanish during 2015-2018

Santamaría

Tuñas

Peces-Barba

et al. 2021

Human Vaccines & Immunotherapeutics

Self Cite

View full text Add to dashboard Cite

Social media, and in particularly Twitter, can be a resource of enormous value to retrieve information about the opinion of general population to vaccines. The increasing popularity of this social media has allowed to use its content to have a clear picture of their users on this topic. In this paper, we perform a study about vaccine-related messages published in Spanish during 2015-2018. More specifically, the paper has focused on two specific diseases: influenza and measles (and MMR as its vaccine). By also including an analysis about the sentiment expressed on the published tweets, we have been able to identify the type of messages that are published on Twitter with respect these two pathologies and their vaccines. Results showed that in contrary on popular opinions, most of the messages published are non-negative. On the other hand, the analysis showed that some messages attracted a huge attention and provoked peaks in the number of published tweets, explaining some changes in the observed trends.

show abstract

Section: General Methodologymentioning

confidence: 98%

“…For this reason, the paper will not dig into the technical details about the tweet sentiment identification, which has been already described with anteriority. 43 …”

Section: General Methodologymentioning

confidence: 99%