Taylor-ChOA: Taylor-Chimp Optimized Random Multimodal Deep Learning-Based Sentiment Classification Model for Course Recommendation
Course recommendation is a key for achievement in a student’s academic path. However, it is challenging to appropriately select course content among numerous online education resources, due to the differences in users’ knowledge structures. Therefore, this paper develops a novel sentiment classifica...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-04-01
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| Series: | Mathematics |
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| Online Access: | https://www.mdpi.com/2227-7390/10/9/1354 |
| _version_ | 1827672172219334656 |
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| author | Santosh Kumar Banbhrani Bo Xu Hongfei Lin Dileep Kumar Sajnani |
| author_facet | Santosh Kumar Banbhrani Bo Xu Hongfei Lin Dileep Kumar Sajnani |
| author_sort | Santosh Kumar Banbhrani |
| collection | DOAJ |
| description | Course recommendation is a key for achievement in a student’s academic path. However, it is challenging to appropriately select course content among numerous online education resources, due to the differences in users’ knowledge structures. Therefore, this paper develops a novel sentiment classification approach for recommending the courses using Taylor-chimp Optimization Algorithm enabled Random Multimodal Deep Learning (Taylor ChOA-based RMDL). Here, the proposed Taylor ChOA is newly devised by the combination of the Taylor concept and Chimp Optimization Algorithm (ChOA). Initially, course review is done to find the optimal course, and thereafter feature extraction is performed for extracting the various significant features needed for further processing. Finally, sentiment classification is done using RMDL, which is trained by the proposed optimization algorithm, named ChOA. Thus, the positively reviewed courses are obtained from the classified sentiments for improving the course recommendation procedure. Extensive experiments are conducted using the E-Khool dataset and Coursera course dataset. Empirical results demonstrate that Taylor ChOA-based RMDL model significantly outperforms state-of-the-art methods for course recommendation tasks. |
| first_indexed | 2024-03-10T03:57:53Z |
| format | Article |
| id | doaj.art-c61f9ceacc6c4a85b1685e1d735520f6 |
| institution | Directory Open Access Journal |
| issn | 2227-7390 |
| language | English |
| last_indexed | 2024-03-10T03:57:53Z |
| publishDate | 2022-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj.art-c61f9ceacc6c4a85b1685e1d735520f62023-11-23T08:43:02ZengMDPI AGMathematics2227-73902022-04-01109135410.3390/math10091354Taylor-ChOA: Taylor-Chimp Optimized Random Multimodal Deep Learning-Based Sentiment Classification Model for Course RecommendationSantosh Kumar Banbhrani0Bo Xu1Hongfei Lin2Dileep Kumar Sajnani3School of Computer Science and Technology, Dalian University of Technology, Ganjingzi District, Dalian 116024, ChinaSchool of Computer Science and Technology, Dalian University of Technology, Ganjingzi District, Dalian 116024, ChinaSchool of Computer Science and Technology, Dalian University of Technology, Ganjingzi District, Dalian 116024, ChinaSchool of Computer Science and Engineering, Southeast University, Nanjing 210096, ChinaCourse recommendation is a key for achievement in a student’s academic path. However, it is challenging to appropriately select course content among numerous online education resources, due to the differences in users’ knowledge structures. Therefore, this paper develops a novel sentiment classification approach for recommending the courses using Taylor-chimp Optimization Algorithm enabled Random Multimodal Deep Learning (Taylor ChOA-based RMDL). Here, the proposed Taylor ChOA is newly devised by the combination of the Taylor concept and Chimp Optimization Algorithm (ChOA). Initially, course review is done to find the optimal course, and thereafter feature extraction is performed for extracting the various significant features needed for further processing. Finally, sentiment classification is done using RMDL, which is trained by the proposed optimization algorithm, named ChOA. Thus, the positively reviewed courses are obtained from the classified sentiments for improving the course recommendation procedure. Extensive experiments are conducted using the E-Khool dataset and Coursera course dataset. Empirical results demonstrate that Taylor ChOA-based RMDL model significantly outperforms state-of-the-art methods for course recommendation tasks.https://www.mdpi.com/2227-7390/10/9/1354chimp optimization algorithmcourse recommendationE-learninglong short-term memoryrandom multimodal deep learningsentiment classification |
| spellingShingle | Santosh Kumar Banbhrani Bo Xu Hongfei Lin Dileep Kumar Sajnani Taylor-ChOA: Taylor-Chimp Optimized Random Multimodal Deep Learning-Based Sentiment Classification Model for Course Recommendation Mathematics chimp optimization algorithm course recommendation E-learning long short-term memory random multimodal deep learning sentiment classification |
| title | Taylor-ChOA: Taylor-Chimp Optimized Random Multimodal Deep Learning-Based Sentiment Classification Model for Course Recommendation |
| title_full | Taylor-ChOA: Taylor-Chimp Optimized Random Multimodal Deep Learning-Based Sentiment Classification Model for Course Recommendation |
| title_fullStr | Taylor-ChOA: Taylor-Chimp Optimized Random Multimodal Deep Learning-Based Sentiment Classification Model for Course Recommendation |
| title_full_unstemmed | Taylor-ChOA: Taylor-Chimp Optimized Random Multimodal Deep Learning-Based Sentiment Classification Model for Course Recommendation |
| title_short | Taylor-ChOA: Taylor-Chimp Optimized Random Multimodal Deep Learning-Based Sentiment Classification Model for Course Recommendation |
| title_sort | taylor choa taylor chimp optimized random multimodal deep learning based sentiment classification model for course recommendation |
| topic | chimp optimization algorithm course recommendation E-learning long short-term memory random multimodal deep learning sentiment classification |
| url | https://www.mdpi.com/2227-7390/10/9/1354 |
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