Akademik Veri Yönetim Sistemi
JOURNAL OF STATISTICS & MANAGEMENT SYSTEMS, cilt.1, sa.1, ss.1-12, 2023 (ESCI)
The Covid-19 pandemic has made telemedicine one of the most relevant topics in recent years, so data mining about telemedicine obtained from Twitter offers a unique opportunity. The motivation of this study is to identify the emotional groupings of Twitter data users opinions for telemedicine using opinion mining techniques such as Sentiment Analysis combined with high-dimensional data classification and prediction methods. Data collected from Twitter in August and September of 2021 related to telemedicine and official World Health Organization and World Bank documents for the years 2018 and 2021, respectively. Sentiment Analysis showed that 56.2% (n=5351 tweets) of the sample had predominantly positive emotions towards telemedicine. Telemedicine, telehealth and ivermection are most frequent words in the word cloud. Twitter data users' opinions consists of nine mood classes (SIL index=0.80) and the differences between these classes are statistically significant in terms of positive (p<0.05), negative (p<0.05) and neutral (p<0.05) emotions. Neural Network (AUC=0.842, F1=0.492) and Random Forest (AUC= 0.841, F1=0.494) are the best predictors of Twitter users’ mood classes compared with other machine learning techniques. Pythagorean tree generated by Random Forest showed that retweet is the best predictor of Twitter users’ opinions emotional classes towards telemedicine. Future studies will create big social media datasets for deep understanding of emotion classes of individuals towards telemedicine technologies.