Akademik Veri Yönetim Sistemi
Disaster Medicine and Public Health Preparedness, cilt.17, sa.7, 2023 (SCI-Expanded)
Objectives: The present study aims to examine coronavirus disease 2019 (COVID-19) vaccination discussions on Twitter in Turkey and conduct sentiment analysis. Methods: The current study performed sentiment analysis of Twitter data with the artificial intelligence (AI) Natural Language Processing (NLP) method. The tweets were retrieved retrospectively from March 10, 2020, when the first COVID-19 case was seen in Turkey, to April 18, 2022. A total of 10,308 tweets accessed. The data were filtered before analysis due to excessive noise. First, the text is tokenized. Many steps were applied in normalizing texts. Tweets about the COVID-19 vaccines were classified according to basic emotion categories using sentiment analysis. The resulting dataset was used for training and testing ML (ML) classifiers. Results: It was determined that 7.50% of the tweeters had positive, 0.59% negative, and 91.91% neutral opinions about the COVID-19 vaccination. When the accuracy values of the ML algorithms used in this study were examined, it was seen that the XGBoost (XGB) algorithm had higher scores. Conclusions: Three of 4 tweets consist of negative and neutral emotions. The responsibility of professional chambers and the public is essential in transforming these neutral and negative feelings into positive ones.