SENTIMENT ANALYSIS OF POST-COVID ONLINE EDUCATION AMONG GEN Z WITH VARIOUS CLASSIFICATION METHODS

The COVID-19 pandemic has significantly changed the education sector, shifting from traditional learning to online learning. Generation Z's perception of online education is influenced by their experience as “Digital Natives” who have been familiar with technology since childhood. However, this sudden transition brings new challenges, such as screen fatigue, lack of social interaction, and difficulty in maintaining learning motivation. Sentiment analysis is an important tool to evaluate their experiences and views on online learning. This study aims to investigate Generation Z's views on online education after the pandemic, utilizing various classification methods. Data was collected from Twitter through scraping technique with specific keywords, resulting in a total of 4,986 data obtained using the Tweet Harvest library in Python programming language. The dataset then went through a preprocessing stage, including data cleaning, case folding, tokenizing, stopword removal, and stemming. Before applying Random Forest, SVM, and Naïve Bayes methods, the data is divided into two parts, namely, 3988 training data and 998 testing data with a ratio of 80:20. The accuracy results show that Naïve Bayes achieved 95.49% on training data and 76.05% on testing data, SVM recorded 94.77% accuracy on training data and 87.33% on testing data, and Random Forest obtained 99.97% accuracy on training data and 92.21% on testing data. This research provides important insights into Generation Z's perceptions of post-COVID-19 online education and learning platforms to improve the effectiveness of online learning and identify student challenges in the digital era.