Authors :
N. Narasimha Rao; V.Srujan; A. Praneeth Surya; D. Siva Teja
Volume/Issue :
Volume 8 - 2023, Issue 4 - April
Google Scholar :
https://bit.ly/3TmGbDi
Scribd :
https://bit.ly/415lQVv
DOI :
https://doi.org/10.5281/zenodo.7902032
Abstract :
The capacity to assess and forecast a variety
of topics, including commercial requirements,
environmental needs, election patterns (polls),
governmental needs, etc., may be added to social media
as an intelligent platform. This inspired us to start a
thorough investigation of public thoughts and opinions
on the COVID-19 epidemic on Twitter. The fundamental
training data were gathered from tweets. Based on this,
we have produced research using ensemble deep
learning algorithms to forecast Twitter views more
accurately than earlier works that do the same task. An
N-gram stacked auto encoder supervised learning
technique is used to extract features first. The collected
features are subsequently used in a classification and
prediction process using an ensemble fusion strategy
comprising certain machine learning algorithms,
including decision trees (DT), support vector machines
(SVM), random forests (RF), and K-nearest neighbors
(KNN). Using both mean and mode approaches, all
individual findings are combined/fused for a superior
forecast. The N-gram stacking encoder we suggest using
in combination with an ensemble machine learning
strategy surpasses all other known competitive
techniques, including bigram auto encoders and unigram
auto encoders. The public has a great deal of trust in
government policy during the third wave, and they
support all measures taken to contain the epidemic,
including widespread participation in vaccine
programmes.. The study's findings may be summarised
by saying that people are getting past their fear of the
disease.
Keywords :
Omicron Sentiment Analysis, N-Gram, Analysis, Social Media, Omicron, Tweets, Twitter, Big Data, Data Analysis.
The capacity to assess and forecast a variety
of topics, including commercial requirements,
environmental needs, election patterns (polls),
governmental needs, etc., may be added to social media
as an intelligent platform. This inspired us to start a
thorough investigation of public thoughts and opinions
on the COVID-19 epidemic on Twitter. The fundamental
training data were gathered from tweets. Based on this,
we have produced research using ensemble deep
learning algorithms to forecast Twitter views more
accurately than earlier works that do the same task. An
N-gram stacked auto encoder supervised learning
technique is used to extract features first. The collected
features are subsequently used in a classification and
prediction process using an ensemble fusion strategy
comprising certain machine learning algorithms,
including decision trees (DT), support vector machines
(SVM), random forests (RF), and K-nearest neighbors
(KNN). Using both mean and mode approaches, all
individual findings are combined/fused for a superior
forecast. The N-gram stacking encoder we suggest using
in combination with an ensemble machine learning
strategy surpasses all other known competitive
techniques, including bigram auto encoders and unigram
auto encoders. The public has a great deal of trust in
government policy during the third wave, and they
support all measures taken to contain the epidemic,
including widespread participation in vaccine
programmes.. The study's findings may be summarised
by saying that people are getting past their fear of the
disease.
Keywords :
Omicron Sentiment Analysis, N-Gram, Analysis, Social Media, Omicron, Tweets, Twitter, Big Data, Data Analysis.
