利用Swin变压器进行高级情绪分析:一种新的范式
Gaurav Kumar Rajput1, Saurabh Kumar Srivastava2, Namit Gupta3
1Research Scholar , Collage of Computing Sciences and Information Technology, Teerthanker Mahaveer University , Moradabad, Uttar Pradesh India.
Cognitive neurodynamics
|December 1, 2025
概括
一个新的Swin-MLP模型通过捕获复杂的文本模式来增强医疗保健情绪分析. 这种混合方法显著优于BERT等现有模型,为医疗和药物审查数据提供了更高的准确性.
科学领域:
- 自然语言处理自然语言处理.
- 医疗保健中的机器学习
背景情况:
- 医疗保健文本数据的复杂性需要先进的情绪分析.
- 现有的模型在专业医疗文本中与本地模式和全球依赖性作斗争.
研究的目的:
- 推出一种新的混合Swin变压器-BiLSTM-空间MLP (Swin-MLP) 模型.
- 改进特征提取和情绪分析性能在特定领域的医疗保健文本.
主要方法:
- 开发了一个Swin-MLP模型,集成层次关注和转移窗口机制.
- 评估了药物审查和医学文本数据集的模型.
- 与基线模型进行性能比较,包括BERT,LSTM和GRU.
主要成果:
- 斯温-MLP模型在准确性,精度,回忆,F1得分和AUC方面表现出卓越的性能.
- 与BERT相比,平均准确度提高了1-2%.
- 统计学意义 (p < 0.05) 证实了该模型的有效性.
结论:
- 斯温-MLP模型对于特定领域的医疗保健情绪分析是强大而高效的.
- 建筑创新有助于显著提高性能.
- 未来的工作包括探索轻量级注意力和多式联络分析.
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