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".

Department of Applied Cybernetics Faculty of Science University of Hradec Králové 50003 Hradec Králové Czech Republic

Department of Computational Mathematics Science and Engineering College of Engineering Michigan State University East Lansing MI 48824 USA

Department of Mathematics Faculty of Science Mansoura University Mansoura 35516 Egypt

Department of Mathematics Faculty of Science University of Hradec Králové 50003 Hradec Králové Czech Republic

Department of Statistics and Operations Research College of Science King Saud University Riyadh 11451 Saudi Arabia

Faculty of Informatics and Computing Singidunum University Danijelova 32 11000 Belgrade Serbia

Faculty of Science and Technology Norwegian University for Life Science 1430 Ås Norway

Institute for Smart Systems Technologies Faculty of Technical Sciences Universitaet Klagenfurt A9020 Klagenfurt Austria

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