Multi-Attention Fusion Modeling for Sentiment Analysis of Educational Big Data
As an important branch of natural language processing, sentiment analysis has received increasing attention. In teaching evaluation, sentiment analysis can help educators discover the true feelings of students about the course in a timely manner and adjust the teaching plan accurately and timely to...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Tsinghua University Press
2020-12-01
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| Series: | Big Data Mining and Analytics |
| Subjects: | |
| Online Access: | https://www.sciopen.com/article/10.26599/BDMA.2020.9020024 |
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| author | Guanlin Zhai Yan Yang Heng Wang Shengdong Du |
| author_facet | Guanlin Zhai Yan Yang Heng Wang Shengdong Du |
| author_sort | Guanlin Zhai |
| collection | DOAJ |
| description | As an important branch of natural language processing, sentiment analysis has received increasing attention. In teaching evaluation, sentiment analysis can help educators discover the true feelings of students about the course in a timely manner and adjust the teaching plan accurately and timely to improve the quality of education and teaching. Aiming at the inefficiency and heavy workload of college curriculum evaluation methods, a Multi-Attention Fusion Modeling (Multi-AFM) is proposed, which integrates global attention and local attention through gating unit control to generate a reasonable contextual representation and achieve improved classification results. Experimental results show that the Multi-AFM model performs better than the existing methods in the application of education and other fields. |
| format | Article |
| id | doaj-art-49b8fecce20d433782eade870a62bfac |
| institution | Kabale University |
| issn | 2096-0654 |
| language | English |
| publishDate | 2020-12-01 |
| publisher | Tsinghua University Press |
| record_format | Article |
| series | Big Data Mining and Analytics |
| spelling | doaj-art-49b8fecce20d433782eade870a62bfac2025-02-02T05:59:19ZengTsinghua University PressBig Data Mining and Analytics2096-06542020-12-013431131910.26599/BDMA.2020.9020024Multi-Attention Fusion Modeling for Sentiment Analysis of Educational Big DataGuanlin Zhai0Yan Yang1Heng Wang2Shengdong Du3<institution>School of Information Science and Technology, National Engineering Laboratory of Integrated Transportation Big Data Application Technology, Southwest Jiaotong University</institution>, <city>Chengdu</city> <postal-code>611756</postal-code>, <country>China</country><institution>School of Information Science and Technology, National Engineering Laboratory of Integrated Transportation Big Data Application Technology, Southwest Jiaotong University</institution>, <city>Chengdu</city> <postal-code>611756</postal-code>, <country>China</country><institution>School of Information Science and Technology, National Engineering Laboratory of Integrated Transportation Big Data Application Technology, Southwest Jiaotong University</institution>, <city>Chengdu</city> <postal-code>611756</postal-code>, <country>China</country><institution>School of Information Science and Technology, National Engineering Laboratory of Integrated Transportation Big Data Application Technology, Southwest Jiaotong University</institution>, <city>Chengdu</city> <postal-code>611756</postal-code>, <country>China</country>As an important branch of natural language processing, sentiment analysis has received increasing attention. In teaching evaluation, sentiment analysis can help educators discover the true feelings of students about the course in a timely manner and adjust the teaching plan accurately and timely to improve the quality of education and teaching. Aiming at the inefficiency and heavy workload of college curriculum evaluation methods, a Multi-Attention Fusion Modeling (Multi-AFM) is proposed, which integrates global attention and local attention through gating unit control to generate a reasonable contextual representation and achieve improved classification results. Experimental results show that the Multi-AFM model performs better than the existing methods in the application of education and other fields.https://www.sciopen.com/article/10.26599/BDMA.2020.9020024educational big datasentiment analysisaspect-levelattention |
| spellingShingle | Guanlin Zhai Yan Yang Heng Wang Shengdong Du Multi-Attention Fusion Modeling for Sentiment Analysis of Educational Big Data Big Data Mining and Analytics educational big data sentiment analysis aspect-level attention |
| title | Multi-Attention Fusion Modeling for Sentiment Analysis of Educational Big Data |
| title_full | Multi-Attention Fusion Modeling for Sentiment Analysis of Educational Big Data |
| title_fullStr | Multi-Attention Fusion Modeling for Sentiment Analysis of Educational Big Data |
| title_full_unstemmed | Multi-Attention Fusion Modeling for Sentiment Analysis of Educational Big Data |
| title_short | Multi-Attention Fusion Modeling for Sentiment Analysis of Educational Big Data |
| title_sort | multi attention fusion modeling for sentiment analysis of educational big data |
| topic | educational big data sentiment analysis aspect-level attention |
| url | https://www.sciopen.com/article/10.26599/BDMA.2020.9020024 |
| work_keys_str_mv | AT guanlinzhai multiattentionfusionmodelingforsentimentanalysisofeducationalbigdata AT yanyang multiattentionfusionmodelingforsentimentanalysisofeducationalbigdata AT hengwang multiattentionfusionmodelingforsentimentanalysisofeducationalbigdata AT shengdongdu multiattentionfusionmodelingforsentimentanalysisofeducationalbigdata |