Sentiment analysis with artificial intelligence to improve the teaching-learning process in the virtual classroom

Introduction: In recent years, the teaching-learning process has been changing from face-to-face to virtual mode progressively worldwide, this was significantly accelerated due to the COVID-19 pandemic, where the classroom went from face-to-face to virtual format, affecting all levels of education, many countries had to make a leap to digital knowledge more out of necessity than technological growth, which leads to seeking solutions to new problems from the virtual environment. Today in the new normal from now on, the virtual environment will be developed in parallel with the face-to-face environment. The objective of this research was to identify the emotional state that students have in the virtual classroom, to allow the teacher to evaluate the perception that students have during their class session and thus improve their teaching-learning strategies in real time. Method: An application in artificial intelligence with neural networks was proposed to capture the emotional state of students in the virtual classroom in real time to show the teacher the perception of their students during the virtual class session. Results: The results obtained allow to show the states of the group of students so that the teacher can perceive the sensation within their students at the time of the class and thus improve their teaching-learning strategies in real time. Conclusions: It is concluded that it is an efficient form of continuous improvement for active learning processes within the classroom in real time.