赋予智力障碍者权力使用集成深度学习架构驱动增强的基于文本的情感分类.
Mohammed Abdullah Al-Hagery1, Hechmi Shili2, Nasser Aljohani3
1Department of Computer Science, College of Computer, Qassim University, Buraydah, Saudi Arabia. hajry@qu.edu.sa.
Scientific reports
|November 4, 2025
概括
这项研究引入了一种新的混合深度学习模型,用于从文本中智能识别情绪. 拟议的方法显著提高了在文本数据中检测情绪的准确性,帮助残疾人进行交流.
科学领域:
- 人工智能的人工智能
- 自然语言处理自然语言处理.
- 计算语言学 计算语言学
背景情况:
- 情绪识别在心理学,医疗保健和人机交互 (HCI) 中至关重要.
- 文本情感识别 (TER) 的传统方法通常依赖于有限的数据,并存在可靠性问题.
- 深度学习 (DL) 的进步推动了TER的重大进展.
研究的目的:
- 开发一个先进的深度学习系统,用于准确的文本情感识别.
- 通过改进的TER来提高残疾人的通信可访问性.
- 从文本中引入智能情绪识别,使用混合深度学习模型和文字嵌入过程 (IERT-HDLMWEP) 模型.
主要方法:
- 文本预处理以减少维度和准备数据进行分析.
- 混合功能表示集成Word2Vec,TF-IDF加权和部分语音功能.
- 使用混合卷积神经网络 (CNN) 和带有注意力机制 (C-BiG-A) 的双向门循环单元 (BiGRU) 的分类.
主要成果:
- IERT-HDLMWEP模型在从文本数据集中检测情绪方面表现出卓越的性能.
- 经验结果证实了拟议的混合深度学习方法的有效性.
- 该方法显示,与现有的TER技术相比,该方法有显著的改进.
结论:
- IERT-HDLMWEP模型为文本情感识别提供了强大而准确的解决方案.
- 这种方法有可能显著帮助有沟通障碍的人.
- 该研究强调了混合深度学习架构在推进NLP任务中的有效性.
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