Analysis of College Students’ Public Opinion Based on Machine Learning and Evolutionary Algorithm
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 peculia...
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| Format: | Article |
| Language: | English |
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Wiley
2019-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2019/1712569 |
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| author | Jinqing Zhang Pengchao Zhang Bin Xu |
| author_facet | Jinqing Zhang Pengchao Zhang Bin Xu |
| author_sort | Jinqing Zhang |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-55c884233d7d416db87349d0715fc2c5 |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2019-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-55c884233d7d416db87349d0715fc2c52025-02-03T06:05:23ZengWileyComplexity1076-27871099-05262019-01-01201910.1155/2019/17125691712569Analysis of College Students’ Public Opinion Based on Machine Learning and Evolutionary AlgorithmJinqing Zhang0Pengchao Zhang1Bin Xu2School of Automation Science and Electrical Engineering, Beihang University, Beijing, ChinaShaanxi Provincial Key Laboratory of Industrial Automation, Shaanxi University of Technology, Hanzhong, Shaanxi 723000, ChinaSchool of Automation, Northwestern Polytechnical University, Xi’an, ChinaThe 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.http://dx.doi.org/10.1155/2019/1712569 |
| spellingShingle | Jinqing Zhang Pengchao Zhang Bin Xu Analysis of College Students’ Public Opinion Based on Machine Learning and Evolutionary Algorithm Complexity |
| title | Analysis of College Students’ Public Opinion Based on Machine Learning and Evolutionary Algorithm |
| title_full | Analysis of College Students’ Public Opinion Based on Machine Learning and Evolutionary Algorithm |
| title_fullStr | Analysis of College Students’ Public Opinion Based on Machine Learning and Evolutionary Algorithm |
| title_full_unstemmed | Analysis of College Students’ Public Opinion Based on Machine Learning and Evolutionary Algorithm |
| title_short | Analysis of College Students’ Public Opinion Based on Machine Learning and Evolutionary Algorithm |
| title_sort | analysis of college students public opinion based on machine learning and evolutionary algorithm |
| url | http://dx.doi.org/10.1155/2019/1712569 |
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