Özet:

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 study to perform sentiment analysis on COVID-19 vaccines in Turkey, and it shows that it is a valuable and easily applied tool to monitor sensitivity to COVID-19 vaccines with sentiment analysis approach over social media.

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