对于具有多头注意力和遗传交叉的情感分析变压器的最佳架构
Wiam Saidi1, Boutaina Satouri2, Abdellatif El Abderrahmani2
1Laboratory, Computer Science, Innovation and Artificial Intelligence (L3IA), Faculty of Sciences, Sidi Mohamed Ben Abdellah University (USMBA), 30000, Fez, Morocco. wiam.saidi@usmba.ac.ma.
Scientific reports
|October 29, 2025
概括
这项研究介绍了一种基于变压器的情感分析新方法,利用进化技术优化模型架构. 该方法显著提高了处理复杂文本数据的准确性和效率.
科学领域:
- 自然语言处理自然语言处理.
- 机器学习 机器学习
- 人工智能的人工智能
背景情况:
- 情绪分析对于战略监控,在线声誉管理和客户满意度至关重要.
- 提高情绪分析模型的准确性和适应性仍然是一个重大挑战.
- 当前的方法经常与复杂的文本数据和概括性作斗争.
研究的目的:
- 提出一种用于提高情绪分析准确性和效率的新方法.
- 将变压器架构与进化优化技术相结合.
- 开发一种更轻便,更高效,更强大的情绪分析模型.
主要方法:
- 使用预先训练的模型来实现丰富的文本表示 (嵌入和语言特征).
- 开发了OAST-MAGC (具有多头注意力和遗传交叉的情感分析变压器的最佳架构).
- 集成的注意力头和遗传交叉的集成动态修剪,以优化最终层重量.
主要成果:
- 实现了95.96%的高精度和96%的F1得分.
- 与现有的情绪分析技术相比,表现出卓越的性能和稳定性.
- 动态修剪和基因交叉显著提高了融合速度和模型效率.
结论:
- 拟议的OAST-MAGC方法在情绪分析准确性和效率方面提供了显著的改进.
- 动态修剪和遗传交叉的整合导致了更普遍和更少的过度装配模型.
- 这种方法为在各种应用中处理复杂文本数据打开了新的可能性.
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