Analysis of College Students' Public Opinion Based on Machine Learning and Evolutionary Algorithm

Abstract

The recent information explosion may have many negative impacts on college students, such as distraction from learning and addiction to meaningless and fake news. To avoid these phenomena, it is necessary to verify the students' state of mind and give them appropriate guidance. However, many peculiarities, including subject focused, multiaspect, and low consistency on different samples' interests, bring great challenges while leveraging the mainstream opinion mining method. To solve this problem, this paper proposes a new way by using a questionnaire which covers most aspects of a student's life to collect comprehensive information and feed the information into a neural network. With reliable prediction on students' state of mind and awareness of feature importance, colleges can give students guidance associated with their own experience and make macroscopic policies more effective. A pipeline is proposed to relieve overfitting during the collected information training. First, the singular value decomposition is used in pretreatment of data set which includes outlier detection and dimension reduction. Then, the genetic algorithm is introduced in the training process to find the proper initial parameters of network, and in this way, it can prevent the network from falling into the local minimum. A method of calculating the importance of students' features is also proposed. The experiment result shows that the new pipeline works well, and the predictor has high accuracy on predicting fresh samples. The design procedure and the prediction design will provide suggestions to deal with students' state of mind and the college's public opinion.