An Improved System for Students Feedback Analysis Using Supervised Probability Techniques

Sentiment analysis is the process of detecting positive or negative sentiment in text. Educational institutes and universities are showing interest to improve quality of education system by monitoring teacher's, teaching, student's learning, and course analysis using feedbacks. It aims to train the existing unstructured feedback and to classify the sampled feedback belongs into positive, negative or neutral polarity based on probability technique and supervised concepts. It consists of four stages input comment set, pre-processing, feature extraction and classifier respectively. In the first stage, the raw datasets are collected from server by N number of user feedback data X. In the stage two, the system reduces number of words in each individual text command to recognize the significant and insignificant words and eliminate the insignificant words based on predetermined model. In the third stage, It aims to reduce the number of features in a data set by creating a new feature from the existing one. In the next stage, the positive, negative and neutral keywords are forwarded by feature extraction and keyword input dataset are mixed with all possible key value based on Naïve Bayes approach and training data. The Experimental result shows that the proposed approach is well suitable to train the unstructured feedback and classify them into positive, negative and neutral polarity with better clarity.