An efficient sentiment analysis technique based on fine-tuned EdBERT for virtual learning environments

A new model called EdBERT is created explicitly for sentiment analysis of evaluations written by virtual learners with unmatched categorization accuracy, boasting the best evaluation matrix to date with accuracy rates of 87.89%, F1-score of 88%, precision of 89%, and recall of 88%.