Sentimental Analysis of COVID-19 Related Messages in Social Networks by Involving an N-Gram Stacked Autoencoder Integrated in an Ensemble Learning Scheme

The current population worldwide extensively uses social media to share thoughts, societal issues, and personal concerns. Social media can be viewed as an intelligent platform that can be augmented with a capability to analyze and predict various issues such as business needs, environmental needs, e...

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Autores principales:	Venkatachalam Kandasamy, Pavel Trojovský, Fadi Al Machot, Kyandoghere Kyamakya, Nebojsa Bacanin, Sameh Askar, Mohamed Abouhawwash
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	COVID-19 data prediction N-gram feature extraction ensemble machine learning twitter data Chemical technology TP1-1185
Acceso en línea:	https://doaj.org/article/9c8e487ad6674a1bb062c2bdcf372393
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Sentimental Analysis of COVID-19 Related Messages in Social Networks by Involving an N-Gram Stacked Autoencoder Integrated in an Ensemble Learning Scheme

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