Faculty Profile Generation and Student Feedback Evaluation-based on Text Summarization and Sentiment Analysis
Neeraj Sharma, Vaibhav Jain · 2024
Student feedback in educational institutions plays very important role in observing the opinions of the students about the course, the teacher teaching the course and the syllabus. Taking feedback from a large group of students for the subjects taught and analyzing them manually is a difficult task. Analyzing student feedback may involve generating an overall feedback report and looking at teacher’s performance for the subjects taught. Educational Data Mining may benefit stakeholders including students and teachers by extracting key patterns from feedback submitted by students regarding teaching, curriculum etc. Here, we propose a feedback evaluation and summarization system which classifies student feedback and accordingly generate faculty profile and feedback summary by making use of sentiment analysis and text summarization techniques. In this paper, these two domains have been combined to achieve our goal. Our proposed system may be integrated with any existing online feedback collection system. For evaluating our proposed system, we have compared different supervised learning techniques and lexicon-based techniques. We examine the results of these techniques to provide a conclusive study as to identify which among this technique is more efficient and further use these results to generate faculty profile based on the feedback evaluated.