Research on university email analysis based on SVM-RFE and Transformer-TBAM
By mining and analyzing email text data from universities, it can help faculty members better understand students’opinions and suggestions, and improve management efficiency. At present, deep learning methods are the main approach for text sentiment analysis, but existing methods have not fully util...
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| Format: | Article |
| Language: | zho |
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Editorial Department of Journal on Communications
2024-11-01
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| Series: | Tongxin xuebao |
| Subjects: | |
| Online Access: | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2024229/ |
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| author | LI Zhen LI Zhichao CHEN Lin |
| author_facet | LI Zhen LI Zhichao CHEN Lin |
| author_sort | LI Zhen |
| collection | DOAJ |
| description | By mining and analyzing email text data from universities, it can help faculty members better understand students’opinions and suggestions, and improve management efficiency. At present, deep learning methods are the main approach for text sentiment analysis, but existing methods have not fully utilized the features in Chinese text. To address this issue, a framework based on SVM-RFE and Transformer models was proposed for processing university emails. This architecture reconstructs a dual branch attention model and feature filtering mechanism to deeply extract effective feature information. The experiment shows that the algorithm achieves an accuracy of 94.67% in the classification of university email datasets, which is 1.2% higher than traditional algorithms. |
| format | Article |
| id | doaj-art-b75fa06dd26440c08695f9a047bf81b0 |
| institution | Kabale University |
| issn | 1000-436X |
| language | zho |
| publishDate | 2024-11-01 |
| publisher | Editorial Department of Journal on Communications |
| record_format | Article |
| series | Tongxin xuebao |
| spelling | doaj-art-b75fa06dd26440c08695f9a047bf81b02025-01-14T08:46:38ZzhoEditorial Department of Journal on CommunicationsTongxin xuebao1000-436X2024-11-01459710179661586Research on university email analysis based on SVM-RFE and Transformer-TBAMLI ZhenLI ZhichaoCHEN LinBy mining and analyzing email text data from universities, it can help faculty members better understand students’opinions and suggestions, and improve management efficiency. At present, deep learning methods are the main approach for text sentiment analysis, but existing methods have not fully utilized the features in Chinese text. To address this issue, a framework based on SVM-RFE and Transformer models was proposed for processing university emails. This architecture reconstructs a dual branch attention model and feature filtering mechanism to deeply extract effective feature information. The experiment shows that the algorithm achieves an accuracy of 94.67% in the classification of university email datasets, which is 1.2% higher than traditional algorithms.http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2024229/SVMcollege emailTransformerattention mechanism |
| spellingShingle | LI Zhen LI Zhichao CHEN Lin Research on university email analysis based on SVM-RFE and Transformer-TBAM Tongxin xuebao SVM college email Transformer attention mechanism |
| title | Research on university email analysis based on SVM-RFE and Transformer-TBAM |
| title_full | Research on university email analysis based on SVM-RFE and Transformer-TBAM |
| title_fullStr | Research on university email analysis based on SVM-RFE and Transformer-TBAM |
| title_full_unstemmed | Research on university email analysis based on SVM-RFE and Transformer-TBAM |
| title_short | Research on university email analysis based on SVM-RFE and Transformer-TBAM |
| title_sort | research on university email analysis based on svm rfe and transformer tbam |
| topic | SVM college email Transformer attention mechanism |
| url | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2024229/ |
| work_keys_str_mv | AT lizhen researchonuniversityemailanalysisbasedonsvmrfeandtransformertbam AT lizhichao researchonuniversityemailanalysisbasedonsvmrfeandtransformertbam AT chenlin researchonuniversityemailanalysisbasedonsvmrfeandtransformertbam |