Authors :
Ahmed Mohammed; Dr. A. Pandian
Volume/Issue :
Volume 7 - 2022, Issue 6 - June
Google Scholar :
https://bit.ly/3IIfn9N
Scribd :
https://bit.ly/3PkTJfY
DOI :
https://doi.org/10.5281/zenodo.6856347
Abstract :
The COVID-19 pandemic has affected a
large number of people, causing great worry, fear, and
conflicting feelings or emotions. It has elevated our
understanding of the world to unprecedented heights.
COVID-19 is rapidly spreading, and the only way to
halt it is for the entire population to be vaccinated.
However, there is still concern about vaccinations
among the general public. From the beginning of
vaccinations, many people have refused to have vaccines
injected into them. Using survey data acquired via
Google form, deep learning techniques were used to
build a model for sentiment classification and prediction
of COVID-19 vaccination. Public perceptions towards
the COVID-19 vaccine were analyzed using natural
language processing (NLP) and deep learning
techniques. The dataset's responses were 42.60%
positive, 35.74% negative, and 21.66% neutral. A
convolutional neural network (CNN) and long shortterm memory (LSTM) were used. The LSTM algorithm
performed better than the CNN algorithm. The average
accuracy scores obtained for CNN and LSTM sentiment
classification and prediction models were 68% and
93%, respectively. As evaluation metrics, accuracy,
precision, recall, and f-measure were used. This
research demonstrates the application of deep learning
techniques to sentiment analysis tasks involving the
COVID-19 vaccine.
Keywords :
Deep Learning, Natural Language Processing, Sentiment Prediction, Covid-19 Vaccination, Convolutional Neural Network, and Long Short-Term Memory.
The COVID-19 pandemic has affected a
large number of people, causing great worry, fear, and
conflicting feelings or emotions. It has elevated our
understanding of the world to unprecedented heights.
COVID-19 is rapidly spreading, and the only way to
halt it is for the entire population to be vaccinated.
However, there is still concern about vaccinations
among the general public. From the beginning of
vaccinations, many people have refused to have vaccines
injected into them. Using survey data acquired via
Google form, deep learning techniques were used to
build a model for sentiment classification and prediction
of COVID-19 vaccination. Public perceptions towards
the COVID-19 vaccine were analyzed using natural
language processing (NLP) and deep learning
techniques. The dataset's responses were 42.60%
positive, 35.74% negative, and 21.66% neutral. A
convolutional neural network (CNN) and long shortterm memory (LSTM) were used. The LSTM algorithm
performed better than the CNN algorithm. The average
accuracy scores obtained for CNN and LSTM sentiment
classification and prediction models were 68% and
93%, respectively. As evaluation metrics, accuracy,
precision, recall, and f-measure were used. This
research demonstrates the application of deep learning
techniques to sentiment analysis tasks involving the
COVID-19 vaccine.
Keywords :
Deep Learning, Natural Language Processing, Sentiment Prediction, Covid-19 Vaccination, Convolutional Neural Network, and Long Short-Term Memory.