Identifying Polarity in Tweets from an Imbalanced Dataset about Diseases and Vaccines Using a Meta-Model Based on Machine Learning Techniques

Sentiment analysis is one of the hottest topics in the area of natural language. It has attracted a huge interest from both the scientific and industrial perspective. Identifying the sentiment expressed in a piece of textual information is a challenging task that several commercial tools have tried...

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Bibliographic Details
Published in:Applied sciences 2020-12, Vol.10 (24), p.9019
Main Authors: Rodríguez-González, Alejandro, Tuñas, Juan Manuel, Prieto Santamaría, Lucia, Fernández Peces-Barba, Diego, Menasalvas Ruiz, Ernestina, Jaramillo, Almudena, Cotarelo, Manuel, Conejo Fernández, Antonio J., Arce, Amalia, Gil, Angel
Format: Article
Language:English
Subjects:
Classification
commercial tools
COVID-19
Human papillomavirus
Learning algorithms
Machine learning
metamodel
Polarity
Sentiment analysis
Social networks
twitter data
Unbalance
unbalanced data
Vaccines
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Summary:Sentiment analysis is one of the hottest topics in the area of natural language. It has attracted a huge interest from both the scientific and industrial perspective. Identifying the sentiment expressed in a piece of textual information is a challenging task that several commercial tools have tried to address. In our aim of capturing the sentiment expressed in a set of tweets retrieved for a study about vaccines and diseases during the period 2015–2018, we found that some of the main commercial tools did not allow an accurate identification of the sentiment expressed in a tweet. For this reason, we aimed to create a meta-model which used the results of the commercial tools to improve the results of the tools individually. As part of this research, we had to deal with the problem of unbalanced data. This paper presents the main results in creating a metal-model from three commercial tools to the correct identification of sentiment in tweets by using different machine-learning techniques and methods and dealing with the unbalanced data problem.
ISSN:2076-3417
2076-3417
DOI:10.3390/app10249019