一个增强的社会情感识别模型,使用双向封闭的反复单元和注意力机制,并使用先进的优化算法
Taghreed Ali Alsudais1, Muhammad Swaileh A Alzaidi2, Majdy M Eltahir3
1Department of Teaching and Learning, College of Education and Human Development, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, Riyadh, 11671, Saudi Arabia.
Scientific reports
|November 21, 2025
概括
社会情感学习 (SEL) 对于学业成功至关重要. 一个新的深度表示模型与词嵌入和优化算法用于社会情感识别 (DRMWE-OASER) 在检测文本中的SEL方面取得了99.50%的准确性.
科学领域:
- 教育教育教育教育教育教育.
- 心理学 心理学 心理学
- 计算机科学 计算机科学
背景情况:
- 社会情感学习 (SEL) 越来越多地被认为对儿童的学业准备和学业成绩的影响.
- SEL涉及将认知,行为和情绪技能整合到日常生活中,以改善福祉和成功.
- 基于学校的SEL课程可以提高心理健康,学业表现,道德发展和公民参与.
研究的目的:
- 提出一种有效的方法,DRMWE-OASER,用于使用先进的计算技术检测社会情感学习.
- 开发一个强大的系统来分析文本数据以识别社会情绪状态.
主要方法:
- 文字预处理以清理和结构化数据.
- 使用TF-IDF进行字体嵌入,用于特征提取.
- 使用具有注意力机制的双向门式循环单元 (BiGRU-AM) 进行分类.
- 通过改进的鱼优化算法 (IWOA) 进行超参数优化.
主要成果:
- 该DRMWE-OASER方法在从文本中检测情绪方面表现出高效率.
- 实验验证产生了高于99.50%的精度,超过了现有模型的性能.
结论:
- 拟议的DRMWE-OASER模型为社会情感学习检测提供了一个高度准确的方法.
- 这种计算方法在教育和心理评估中具有很大的应用潜力.
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