LCANet: a model for analysis of students real-time sentiment by integrating attention mechanism and joint loss function
Abstract By recognizing students’ facial expressions in actual classroom situations, the students’ emotional states can be quickly uncovered, which can help teachers grasp the students’ learning rate, which allows teachers to adjust their teaching strategies and methods, thus improving the quality a...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Springer
2024-11-01
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| Series: | Complex & Intelligent Systems |
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| Online Access: | https://doi.org/10.1007/s40747-024-01608-8 |
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| _version_ | 1832571199374229504 |
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| author | Pengyun Hu Xianpiao Tang Liu Yang Chuijian Kong Daoxun Xia |
| author_facet | Pengyun Hu Xianpiao Tang Liu Yang Chuijian Kong Daoxun Xia |
| author_sort | Pengyun Hu |
| collection | DOAJ |
| description | Abstract By recognizing students’ facial expressions in actual classroom situations, the students’ emotional states can be quickly uncovered, which can help teachers grasp the students’ learning rate, which allows teachers to adjust their teaching strategies and methods, thus improving the quality and effectiveness of classroom teaching. However, most previous facial expression recognition methods have problems such as missing key facial features and imbalanced class distributions in the dateset, resulting in low recognition accuracy. To address these challenges, this paper proposes LCANet, a model founded on a fused attention mechanism and a joint loss function, which allows the recognition of students’ emotions in real classroom scenarios. The model uses ConvNeXt V2 as the backbone network to optimize the global feature extraction capability of the model, and at the same time, it enables the model to pay closer attention to the key regions in facial expressions. We incorporate an improved Channel Spatial Attention (CSA) module as a way to extract more local feature information. Furthermore, to mitigate the class distribution imbalance problem in the facial expression dataset, we introduce a joint loss function. The experimental results show that our LCANet model has good recognition rates on both the public emotion datasets FERPlus, RAF-DB and AffectNet, with accuracies of 91.43%, 90.03% and 64.43%, respectively, with good robustness and generalizability. Additionally, we conducted experiments using the model in real classroom scenarios, detecting and accurately predicting students’ classroom emotions in real time, which provides an important reference for improving teaching in smart teaching scenarios. |
| format | Article |
| id | doaj-art-d060930d4167411184964fae4fed87af |
| institution | Kabale University |
| issn | 2199-4536 2198-6053 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | Springer |
| record_format | Article |
| series | Complex & Intelligent Systems |
| spelling | doaj-art-d060930d4167411184964fae4fed87af2025-02-02T12:50:24ZengSpringerComplex & Intelligent Systems2199-45362198-60532024-11-0111111310.1007/s40747-024-01608-8LCANet: a model for analysis of students real-time sentiment by integrating attention mechanism and joint loss functionPengyun Hu0Xianpiao Tang1Liu Yang2Chuijian Kong3Daoxun Xia4School of Big Data and Computer Science, Guizhou Normal UniversitySchool of Big Data and Computer Science, Guizhou Normal UniversitySchool of Big Data and Computer Science, Guizhou Normal UniversitySchool of Big Data and Computer Science, Guizhou Normal UniversityGuizhou Key Laboratory of Advanced Computing, Guizhou Normal UniversityAbstract By recognizing students’ facial expressions in actual classroom situations, the students’ emotional states can be quickly uncovered, which can help teachers grasp the students’ learning rate, which allows teachers to adjust their teaching strategies and methods, thus improving the quality and effectiveness of classroom teaching. However, most previous facial expression recognition methods have problems such as missing key facial features and imbalanced class distributions in the dateset, resulting in low recognition accuracy. To address these challenges, this paper proposes LCANet, a model founded on a fused attention mechanism and a joint loss function, which allows the recognition of students’ emotions in real classroom scenarios. The model uses ConvNeXt V2 as the backbone network to optimize the global feature extraction capability of the model, and at the same time, it enables the model to pay closer attention to the key regions in facial expressions. We incorporate an improved Channel Spatial Attention (CSA) module as a way to extract more local feature information. Furthermore, to mitigate the class distribution imbalance problem in the facial expression dataset, we introduce a joint loss function. The experimental results show that our LCANet model has good recognition rates on both the public emotion datasets FERPlus, RAF-DB and AffectNet, with accuracies of 91.43%, 90.03% and 64.43%, respectively, with good robustness and generalizability. Additionally, we conducted experiments using the model in real classroom scenarios, detecting and accurately predicting students’ classroom emotions in real time, which provides an important reference for improving teaching in smart teaching scenarios.https://doi.org/10.1007/s40747-024-01608-8Real-time sentiment analysis of studentsAttention mechanismFacial expression recognitionJoint loss function |
| spellingShingle | Pengyun Hu Xianpiao Tang Liu Yang Chuijian Kong Daoxun Xia LCANet: a model for analysis of students real-time sentiment by integrating attention mechanism and joint loss function Complex & Intelligent Systems Real-time sentiment analysis of students Attention mechanism Facial expression recognition Joint loss function |
| title | LCANet: a model for analysis of students real-time sentiment by integrating attention mechanism and joint loss function |
| title_full | LCANet: a model for analysis of students real-time sentiment by integrating attention mechanism and joint loss function |
| title_fullStr | LCANet: a model for analysis of students real-time sentiment by integrating attention mechanism and joint loss function |
| title_full_unstemmed | LCANet: a model for analysis of students real-time sentiment by integrating attention mechanism and joint loss function |
| title_short | LCANet: a model for analysis of students real-time sentiment by integrating attention mechanism and joint loss function |
| title_sort | lcanet a model for analysis of students real time sentiment by integrating attention mechanism and joint loss function |
| topic | Real-time sentiment analysis of students Attention mechanism Facial expression recognition Joint loss function |
| url | https://doi.org/10.1007/s40747-024-01608-8 |
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