A Study on Advancing the Assessment of Teaching Effectiveness for Postgraduate Students through Sentiment Analysis Utilizing Deep Learning Technology (International Journal of Food and Nutritional Sciences, 2022)
This study assists in identifying the faculty's strengths and weaknesses based on positive and negative comments from students in English or Hinglish. The suggested system extracts qualitative data from teachers' quantitative data and emotional scores from numerical response scores during evaluation. It graphically depicts the evaluation results, including the percentage of favorable and negative input from the pupils. This will raise awareness among school officials and teachers about their students' thoughts and worries. This feedback not only benefits university administrators and instructors, but it also has a significant impact on students' decisions about which institutions to attend and which courses to take. Our suggested sentiment analysis approach improves teaching and learning quality by addressing transient emotions and feelings while assessing multilingual students' views of teacher effectiveness and course satisfaction.