Sentiment Analysis of Online Course Evaluation Based on a New Ensemble Deep Learning Mode: Evidence from Chinese
In recent years, online course learning has gradually become the mainstream of learning. As the key data reflecting the quality of online courses, users’ comments are very important for improving the quality of online courses. The sentiment information contained in comments is the guide of course im...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2021-11-01
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| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/11/23/11313 |
| _version_ | 1797508033002078208 |
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| author | Xiaomin Pu Guangxi Yan Chengqing Yu Xiwei Mi Chengming Yu |
| author_facet | Xiaomin Pu Guangxi Yan Chengqing Yu Xiwei Mi Chengming Yu |
| author_sort | Xiaomin Pu |
| collection | DOAJ |
| description | In recent years, online course learning has gradually become the mainstream of learning. As the key data reflecting the quality of online courses, users’ comments are very important for improving the quality of online courses. The sentiment information contained in comments is the guide of course improvement. A new ensemble model is proposed for sentiment analysis. The model takes full advantage of Word2Vec and Glove in word vector representation, and utilizes the bidirectional long and short time network and convolutional neural network to achieve deep feature extraction. Moreover, the multi-objective gray wolf optimization (MOGWO) ensemble method is adopted to integrate the models mentioned above. The experimental results show that the sentiment recognition accuracy of the proposed model is higher than that of the other seven comparison models, with an F1score over 91%, and the recognition results of different emotion levels indicate the stability of the proposed ensemble model. |
| first_indexed | 2024-03-10T04:56:51Z |
| format | Article |
| id | doaj.art-0aac4b479a194dd5a234235cf1109dee |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T04:56:51Z |
| publishDate | 2021-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-0aac4b479a194dd5a234235cf1109dee2023-11-23T02:06:08ZengMDPI AGApplied Sciences2076-34172021-11-0111231131310.3390/app112311313Sentiment Analysis of Online Course Evaluation Based on a New Ensemble Deep Learning Mode: Evidence from ChineseXiaomin Pu0Guangxi Yan1Chengqing Yu2Xiwei Mi3Chengming Yu4College of Information Engineering, Hunan Industry Polytechnic, Changsha 410036, ChinaSchool of Traffic and Transportation Engineering, Central South University, Changsha 410075, ChinaSchool of Traffic and Transportation Engineering, Central South University, Changsha 410075, ChinaSchool of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, ChinaSchool of Traffic and Transportation Engineering, Central South University, Changsha 410075, ChinaIn recent years, online course learning has gradually become the mainstream of learning. As the key data reflecting the quality of online courses, users’ comments are very important for improving the quality of online courses. The sentiment information contained in comments is the guide of course improvement. A new ensemble model is proposed for sentiment analysis. The model takes full advantage of Word2Vec and Glove in word vector representation, and utilizes the bidirectional long and short time network and convolutional neural network to achieve deep feature extraction. Moreover, the multi-objective gray wolf optimization (MOGWO) ensemble method is adopted to integrate the models mentioned above. The experimental results show that the sentiment recognition accuracy of the proposed model is higher than that of the other seven comparison models, with an F1score over 91%, and the recognition results of different emotion levels indicate the stability of the proposed ensemble model.https://www.mdpi.com/2076-3417/11/23/11313two-channel word vectordeep features miningattentionmulti-objective optimization ensemble |
| spellingShingle | Xiaomin Pu Guangxi Yan Chengqing Yu Xiwei Mi Chengming Yu Sentiment Analysis of Online Course Evaluation Based on a New Ensemble Deep Learning Mode: Evidence from Chinese Applied Sciences two-channel word vector deep features mining attention multi-objective optimization ensemble |
| title | Sentiment Analysis of Online Course Evaluation Based on a New Ensemble Deep Learning Mode: Evidence from Chinese |
| title_full | Sentiment Analysis of Online Course Evaluation Based on a New Ensemble Deep Learning Mode: Evidence from Chinese |
| title_fullStr | Sentiment Analysis of Online Course Evaluation Based on a New Ensemble Deep Learning Mode: Evidence from Chinese |
| title_full_unstemmed | Sentiment Analysis of Online Course Evaluation Based on a New Ensemble Deep Learning Mode: Evidence from Chinese |
| title_short | Sentiment Analysis of Online Course Evaluation Based on a New Ensemble Deep Learning Mode: Evidence from Chinese |
| title_sort | sentiment analysis of online course evaluation based on a new ensemble deep learning mode evidence from chinese |
| topic | two-channel word vector deep features mining attention multi-objective optimization ensemble |
| url | https://www.mdpi.com/2076-3417/11/23/11313 |
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