Research Information System
JOURNAL OF THE FACULTY OF ENGINEERING AND ARCHITECTURE OF GAZI UNIVERSITY, vol.38, no.2, pp.1093-1104, 2023 (SCI-Expanded)
With the rise of social media platforms, which have billions of users around the World, the dissemination of
information has become easier than ever. The COVID-19 pandemic has increased the use of social media to
discuss many topics, including vaccines. The aim of this study is to analyze public sentiment with Machine
Learning of vaccine-related tweets obtained on Twitter in order to better understand the attitudes and
concerns of social media users, especially regarding COVID-19 vaccines in Turkey. For this purpose, the
majority voting method, which is an ensemble learning method, was developed by comparing the machine
learning algorithm used in six different classification tasks and then via Support Vector Machine, XGBoost
and Random Forest having the highest accuracy, in the study. Soft Voting method, which is one of the
majority voting methods, has reached a success rate of 90.5%, with a higher success rate than both the Hard
Voting approach and the other six individual machine learning approaches. With the Soft Voting method,
which has the highest accuracy rate, 412,588 daily tweets from 153 days obtained from Twitter were
analyzed and the results were reported. The findings of the study are very striking and differ from studies on
other countries. As far as we know, this study is the first in Turkey to perform sentiment analysis on COVID19 vaccines. In addition, the findings of the study show that the proposed method is a valuable and easily
applied tool to monitor the sensitivity of COVID-19 vaccines with a sentiment analysis approach via social
media.