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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id	oai:doaj.org-article:9c8e487ad6674a1bb062c2bdcf372393
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spelling	oai:doaj.org-article:9c8e487ad6674a1bb062c2bdcf3723932021-11-25T18:57:36ZSentimental Analysis of COVID-19 Related Messages in Social Networks by Involving an N-Gram Stacked Autoencoder Integrated in an Ensemble Learning Scheme10.3390/s212275821424-8220https://doaj.org/article/9c8e487ad6674a1bb062c2bdcf3723932021-11-01T00:00:00Zhttps://www.mdpi.com/1424-8220/21/22/7582https://doaj.org/toc/1424-8220The 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, election trends (polls), governmental needs, etc. This has motivated us to initiate a comprehensive search of the COVID-19 pandemic-related views and opinions amongst the population on Twitter. The basic training data have been collected from Twitter posts. On this basis, we have developed research involving ensemble deep learning techniques to reach a better prediction of the future evolutions of views in Twitter when compared to previous works that do the same. First, feature extraction is performed through an N-gram stacked autoencoder supervised learning algorithm. The extracted features are then involved in a classification and prediction involving an ensemble fusion scheme of selected machine learning techniques such as decision tree (DT), support vector machine (SVM), random forest (RF), and K-nearest neighbour (KNN). all individual results are combined/fused for a better prediction by using both mean and mode techniques. Our proposed scheme of an N-gram stacked encoder integrated in an ensemble machine learning scheme outperforms all the other existing competing techniques such unigram autoencoder, bigram autoencoder, etc. Our experimental results have been obtained from a comprehensive evaluation involving a dataset extracted from open-source data available from Twitter that were filtered by using the keywords “covid”, “covid19”, “coronavirus”, “covid-19”, “sarscov2”, and “covid_19”.Venkatachalam KandasamyPavel TrojovskýFadi Al MachotKyandoghere KyamakyaNebojsa BacaninSameh AskarMohamed AbouhawwashMDPI AGarticleCOVID-19data predictionN-gram feature extractionensemble machine learningtwitter dataChemical technologyTP1-1185ENSensors, Vol 21, Iss 7582, p 7582 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	COVID-19 data prediction N-gram feature extraction ensemble machine learning twitter data Chemical technology TP1-1185
spellingShingle	COVID-19 data prediction N-gram feature extraction ensemble machine learning twitter data Chemical technology TP1-1185 Venkatachalam Kandasamy Pavel Trojovský Fadi Al Machot Kyandoghere Kyamakya Nebojsa Bacanin Sameh Askar Mohamed Abouhawwash Sentimental Analysis of COVID-19 Related Messages in Social Networks by Involving an N-Gram Stacked Autoencoder Integrated in an Ensemble Learning Scheme
description	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, election trends (polls), governmental needs, etc. This has motivated us to initiate a comprehensive search of the COVID-19 pandemic-related views and opinions amongst the population on Twitter. The basic training data have been collected from Twitter posts. On this basis, we have developed research involving ensemble deep learning techniques to reach a better prediction of the future evolutions of views in Twitter when compared to previous works that do the same. First, feature extraction is performed through an N-gram stacked autoencoder supervised learning algorithm. The extracted features are then involved in a classification and prediction involving an ensemble fusion scheme of selected machine learning techniques such as decision tree (DT), support vector machine (SVM), random forest (RF), and K-nearest neighbour (KNN). all individual results are combined/fused for a better prediction by using both mean and mode techniques. Our proposed scheme of an N-gram stacked encoder integrated in an ensemble machine learning scheme outperforms all the other existing competing techniques such unigram autoencoder, bigram autoencoder, etc. Our experimental results have been obtained from a comprehensive evaluation involving a dataset extracted from open-source data available from Twitter that were filtered by using the keywords “covid”, “covid19”, “coronavirus”, “covid-19”, “sarscov2”, and “covid_19”.
format	article
author	Venkatachalam Kandasamy Pavel Trojovský Fadi Al Machot Kyandoghere Kyamakya Nebojsa Bacanin Sameh Askar Mohamed Abouhawwash
author_facet	Venkatachalam Kandasamy Pavel Trojovský Fadi Al Machot Kyandoghere Kyamakya Nebojsa Bacanin Sameh Askar Mohamed Abouhawwash
author_sort	Venkatachalam Kandasamy
title	Sentimental Analysis of COVID-19 Related Messages in Social Networks by Involving an N-Gram Stacked Autoencoder Integrated in an Ensemble Learning Scheme
title_short	Sentimental Analysis of COVID-19 Related Messages in Social Networks by Involving an N-Gram Stacked Autoencoder Integrated in an Ensemble Learning Scheme
title_full	Sentimental Analysis of COVID-19 Related Messages in Social Networks by Involving an N-Gram Stacked Autoencoder Integrated in an Ensemble Learning Scheme
title_fullStr	Sentimental Analysis of COVID-19 Related Messages in Social Networks by Involving an N-Gram Stacked Autoencoder Integrated in an Ensemble Learning Scheme
title_full_unstemmed	Sentimental Analysis of COVID-19 Related Messages in Social Networks by Involving an N-Gram Stacked Autoencoder Integrated in an Ensemble Learning Scheme
title_sort	sentimental analysis of covid-19 related messages in social networks by involving an n-gram stacked autoencoder integrated in an ensemble learning scheme
publisher	MDPI AG
publishDate	2021
url	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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