Publication: Sentimental Analysis of COVID-19 Relate...

Master data
Title: Sentimental Analysis of COVID-19 Related Messages in Social Networks by Involving an N-Gram Stacked Autoencoder Integrated in an Ensemble Learning Scheme
Subtitle:
Abstract: 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 ma-chine 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”.
Keywords: Electrical and Electronic Engineering, Biochemistry, Instrumentation, Atomic and Molecular Physics, and Optics, Analytical Chemistry
Publication type: Article in journal (Authorship)
Publication date: 15.11.2021 (Online)
Published by: Sensors
Sensors
to publication
 ( MDPI Publishing; )
Title of the series: -
Volume number: 21
Issue: 22
First publication: Yes
Version: -
Page: -
Total number of pages: 7582 pp.

Versionen
Keine Version vorhanden
Publication date: 15.11.2021
ISBN (e-book): -
eISSN: 1424-8220
DOI: http://dx.doi.org/10.3390/s21227582
Homepage: https://www.mdpi.com/1424-8220/21/22/7582
Open access
  • Available online (open access)

Assignment
Organisation Address
Fakultät für Technische Wissenschaften
  Institut für Intelligente Systemtechnologien
Universitätsstraße 65-67
9020 Klagenfurt am Wörthersee
Austria
   hubert.zangl@aau.at
http://www.uni-klu.ac.at/tewi/ict/sst/index.html
To organisation
Universitätsstraße 65-67
AT - 9020  Klagenfurt am Wörthersee

Categorisation
Subject areas
  • 102018 - Artificial neural networks
  • 102019 - Machine learning
Research Cluster
  • Self-organizing systems
  • Humans in the Digital Age
Citation index
  • Science Citation Index (SCI)
Information about the citation index: Master Journal List
Peer reviewed
  • Yes
Publication focus
  • Science to Science (Quality indicator: I)
Classification raster of the assigned organisational units:
working groups
  • Transportation Informatics Group

Cooperations
Organisation Address
University of Hradec Králové, Faculty of Science,
50003 Hradec Králové
Czechia
CZ - 50003  Hradec Králové

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