BibTeX
@article{IJASEIT14724,
author = {Malak Aljabri and Sumayh S. Aljameel and Irfan Ullah Khan and Nida Aslam and Sara Mhd. Bachar Charouf and Norah Alzahrani},
title = {Machine Learning Model for Sentiment Analysis of COVID-19 Tweets},
journal = {International Journal on Advanced Science, Engineering and Information Technology},
volume = {12},
number = {3},
year = {2022},
pages = {1206--1214},
keywords = {Sentiment analysis; Twitter; Covid-19; machine learning.},
abstract = {Covid-19 pandemic presents unprecedented challenges and enormously affects different aspects of individuals' lives worldwide. The implementation of different prevention measures, the economic and social disruption, and the significant rise in the mortality rate greatly affect the peoples' spectrum of emotions. Sentiment analysis, an important branch of artificial intelligence, uses machine learning techniques to understand public perspectives and gain more insights into how they think and feel. During the pandemic, sentiment analysis increasingly contributes towards making appropriate decisions. This research aims to analyze the public sentiment related to Covid-19 by exploring social perceptions shared on Twitter, one of the most ubiquitous social networks. This goal was achieved by building a machine learning model using a dataset of Covid-19 related English tweets. Different combinations of machine learning classification algorithms (Support Vector Machine (SVM), Random Forest (RF), and XGBoost (XGB)) and feature extraction techniques (Term Frequency-Inverse Document Frequency (TF-IDF) and N-gram) were built and applied to the dataset for binary (positive, negative) and ternary (positive, negative, and neutral) classifications. A comparative study for the performance of the different models was then conducted, and the results concluded that XGB classification algorithm with unigram and bigram for binary classification achieved the highest accuracy of 90%. This sentiment analysis model can assist countries and governments in measuring the impact of the pandemic and the applied prevention measures on people's emotional and mental health and take early actions to reduce their impact or prevent them from becoming severe cases.},
issn = {2088-5334},
publisher = {INSIGHT - Indonesian Society for Knowledge and Human Development},
url = {http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=14724},
doi = {10.18517/ijaseit.12.3.14724}
}
EndNote
%A Aljabri, Malak
%A Aljameel, Sumayh S.
%A Khan, Irfan Ullah
%A Aslam, Nida
%A Charouf, Sara Mhd. Bachar
%A Alzahrani, Norah
%D 2022
%T Machine Learning Model for Sentiment Analysis of COVID-19 Tweets
%B 2022
%9 Sentiment analysis; Twitter; Covid-19; machine learning.
%! Machine Learning Model for Sentiment Analysis of COVID-19 Tweets
%K Sentiment analysis; Twitter; Covid-19; machine learning.
%X Covid-19 pandemic presents unprecedented challenges and enormously affects different aspects of individuals' lives worldwide. The implementation of different prevention measures, the economic and social disruption, and the significant rise in the mortality rate greatly affect the peoples' spectrum of emotions. Sentiment analysis, an important branch of artificial intelligence, uses machine learning techniques to understand public perspectives and gain more insights into how they think and feel. During the pandemic, sentiment analysis increasingly contributes towards making appropriate decisions. This research aims to analyze the public sentiment related to Covid-19 by exploring social perceptions shared on Twitter, one of the most ubiquitous social networks. This goal was achieved by building a machine learning model using a dataset of Covid-19 related English tweets. Different combinations of machine learning classification algorithms (Support Vector Machine (SVM), Random Forest (RF), and XGBoost (XGB)) and feature extraction techniques (Term Frequency-Inverse Document Frequency (TF-IDF) and N-gram) were built and applied to the dataset for binary (positive, negative) and ternary (positive, negative, and neutral) classifications. A comparative study for the performance of the different models was then conducted, and the results concluded that XGB classification algorithm with unigram and bigram for binary classification achieved the highest accuracy of 90%. This sentiment analysis model can assist countries and governments in measuring the impact of the pandemic and the applied prevention measures on people's emotional and mental health and take early actions to reduce their impact or prevent them from becoming severe cases.
%U http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=14724
%R doi:10.18517/ijaseit.12.3.14724
%J International Journal on Advanced Science, Engineering and Information Technology
%V 12
%N 3
%@ 2088-5334
IEEE
Malak Aljabri,Sumayh S. Aljameel,Irfan Ullah Khan,Nida Aslam,Sara Mhd. Bachar Charouf and Norah Alzahrani,"Machine Learning Model for Sentiment Analysis of COVID-19 Tweets," International Journal on Advanced Science, Engineering and Information Technology, vol. 12, no. 3, pp. 1206-1214, 2022. [Online]. Available: http://dx.doi.org/10.18517/ijaseit.12.3.14724.
RefMan/ProCite (RIS)
TY - JOUR
AU - Aljabri, Malak
AU - Aljameel, Sumayh S.
AU - Khan, Irfan Ullah
AU - Aslam, Nida
AU - Charouf, Sara Mhd. Bachar
AU - Alzahrani, Norah
PY - 2022
TI - Machine Learning Model for Sentiment Analysis of COVID-19 Tweets
JF - International Journal on Advanced Science, Engineering and Information Technology; Vol. 12 (2022) No. 3
Y2 - 2022
SP - 1206
EP - 1214
SN - 2088-5334
PB - INSIGHT - Indonesian Society for Knowledge and Human Development
KW - Sentiment analysis; Twitter; Covid-19; machine learning.
N2 - Covid-19 pandemic presents unprecedented challenges and enormously affects different aspects of individuals' lives worldwide. The implementation of different prevention measures, the economic and social disruption, and the significant rise in the mortality rate greatly affect the peoples' spectrum of emotions. Sentiment analysis, an important branch of artificial intelligence, uses machine learning techniques to understand public perspectives and gain more insights into how they think and feel. During the pandemic, sentiment analysis increasingly contributes towards making appropriate decisions. This research aims to analyze the public sentiment related to Covid-19 by exploring social perceptions shared on Twitter, one of the most ubiquitous social networks. This goal was achieved by building a machine learning model using a dataset of Covid-19 related English tweets. Different combinations of machine learning classification algorithms (Support Vector Machine (SVM), Random Forest (RF), and XGBoost (XGB)) and feature extraction techniques (Term Frequency-Inverse Document Frequency (TF-IDF) and N-gram) were built and applied to the dataset for binary (positive, negative) and ternary (positive, negative, and neutral) classifications. A comparative study for the performance of the different models was then conducted, and the results concluded that XGB classification algorithm with unigram and bigram for binary classification achieved the highest accuracy of 90%. This sentiment analysis model can assist countries and governments in measuring the impact of the pandemic and the applied prevention measures on people's emotional and mental health and take early actions to reduce their impact or prevent them from becoming severe cases.
UR - http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=14724
DO - 10.18517/ijaseit.12.3.14724
RefWorks
RT Journal Article
ID 14724
A1 Aljabri, Malak
A1 Aljameel, Sumayh S.
A1 Khan, Irfan Ullah
A1 Aslam, Nida
A1 Charouf, Sara Mhd. Bachar
A1 Alzahrani, Norah
T1 Machine Learning Model for Sentiment Analysis of COVID-19 Tweets
JF International Journal on Advanced Science, Engineering and Information Technology
VO 12
IS 3
YR 2022
SP 1206
OP 1214
SN 2088-5334
PB INSIGHT - Indonesian Society for Knowledge and Human Development
K1 Sentiment analysis; Twitter; Covid-19; machine learning.
AB Covid-19 pandemic presents unprecedented challenges and enormously affects different aspects of individuals' lives worldwide. The implementation of different prevention measures, the economic and social disruption, and the significant rise in the mortality rate greatly affect the peoples' spectrum of emotions. Sentiment analysis, an important branch of artificial intelligence, uses machine learning techniques to understand public perspectives and gain more insights into how they think and feel. During the pandemic, sentiment analysis increasingly contributes towards making appropriate decisions. This research aims to analyze the public sentiment related to Covid-19 by exploring social perceptions shared on Twitter, one of the most ubiquitous social networks. This goal was achieved by building a machine learning model using a dataset of Covid-19 related English tweets. Different combinations of machine learning classification algorithms (Support Vector Machine (SVM), Random Forest (RF), and XGBoost (XGB)) and feature extraction techniques (Term Frequency-Inverse Document Frequency (TF-IDF) and N-gram) were built and applied to the dataset for binary (positive, negative) and ternary (positive, negative, and neutral) classifications. A comparative study for the performance of the different models was then conducted, and the results concluded that XGB classification algorithm with unigram and bigram for binary classification achieved the highest accuracy of 90%. This sentiment analysis model can assist countries and governments in measuring the impact of the pandemic and the applied prevention measures on people's emotional and mental health and take early actions to reduce their impact or prevent them from becoming severe cases.
LK http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=14724
DO - 10.18517/ijaseit.12.3.14724
