An Efficient Sentiment Analysis on Feedback Assessment from Student to Provide Better Education

Opinion mining concept is deployed to predict the trainer evaluation with student's feedback. To examine this feedback concept, where opinion examination helps to distinguish how students are communicated in writings and whether the articulations demonstrate positive (ideal) or negative (troublesome) and conclusions toward the subject. In this research work efficient fusion based neural network (EF-NN) classifier is introduced to predict the frequent context patterns used in the student feedback dataset. Our proposed EF-NN is a hybrid model of both support vector machine and convolutional neural network. Student feedback data set is extracted based on attribute features like the interaction between the students, examination, and notes given, etc., Experimental results can be evaluated on weka toolbox based on this result negative and positive details are collected to improve the efficiency of teaching by faculty to provide the enhanced training. Finally, the result of the accuracy, recall, and precision is compared with the existing K-means clustering method.