Learning-Related Sentiment Detection, Classification, and Application for a Quality Education Using Artificial Intelligence Techniques

Quality education is one of the primary objectives any nation-building strategy and seventeen Sustainable Development Goals (SDGs) by United Nations. To provide quality education, delivering top-quality content not enough. However, understanding learners’ emotions during learning process equally important. most this research work uses general data accessed from Twitter or other publicly available databases. These databases are generally an ideal representation actual sentiments about process. This has collected real learners, mainly undergraduate university students different regions cultures. By analyzing students, appropriate steps can be suggested to improve they receive. In order understand emotions, XLNet technique used. It investigated transfer method adopt efficient model for sentiment detection classification based on data. An experiment shows that proposed approach outperforms aspect enhanced analysis topic in online community.