Advances in natural language processing (NLP) and educational technology, as well as the availability of unprecedented amounts of educationally-relevant text and speech data, have led to an increasing interest in using NLP to address the needs of teachers and students. Educational applications differ in many ways, however, from the types of applications for which NLP systems are typically developed. This paper will organize and give an overview of research in this area, focusing on opportunities as well as challenges. In the proposed methodology, we are collecting student feedback about classroom, exam and lab facilities. After that, we are predicting the dataset using natural language processing. In NLP, we will use 2 approaches: NLTK toolkit and the random forest algorithm approach. After predicting the data, we will be analyzing the positive, negative and neutral nature of the data. This will help in effectively improving the performance of the institution and to create plans to enhance institutions’ teaching and learning experience. Student’s feedback improves communication between the lecturer and the students, allowing the lecturer to have an overall summary of the student’s opinion.