Open AccessSentiment analysis of Twitter posts related to the COVID-19 vaccines
Author(s) -
Noralhuda N. Alabid,
Zainab Dalaf Katheeth
Publication year - 2021
Publication title -
indonesian journal of electrical engineering and computer science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.241
H-Index - 17
eISSN - 2502-4760
pISSN - 2502-4752
DOI - 10.11591/ijeecs.v24.i3.pp1727-1734
Subject(s) - support vector machine , naive bayes classifier , sentiment analysis , covid-19 , public opinion , computer science , artificial intelligence , public health , filter (signal processing) , machine learning , classifier (uml) , subject (documents) , disease , data science , infectious disease (medical specialty) , internet privacy , medicine , political science , world wide web , pathology , politics , law , computer vision , nursing
A real threat to the people of the world has appeared as a result of the spread of the Coronavirus disease of 2019 (COVID-19) disease. A lot of scientific and financial support has been made to devote vaccines capable of ending this epidemic. However, these vaccines have become a subject of debate between individuals, as some people tend to support taking vaccines and others rejecting them. This paper aims to create a framework model to classify the sentiment and opinions of individuals that published in Twitter regarding the COVID-19 vaccines. Identify those opinions can help public health institutions to know public opinions and direct their efforts towards promoting taking vaccinations. Two of the machines learning classification models which are the support vector machine (SVM) and naive Bayes (NB) classifier are applied here. Other pre-processing methods were applied as well to filter unstructured tweets.