多式联络情绪分析利用深度神经网络的强度,并通过XGBoost分类器进行增强
Ganesh Chandrasekaran1, S Dhanasekaran2, C Moorthy3
1Department of Computer and Communication Engineering, Sri Eshwar College of Engineering, Coimbatore, Tamil Nadu, India.
Computer methods in biomechanics and biomedical engineering
|February 10, 2024
概括
这项研究引入了用于多式联络情绪分析的混合LXGB模型,通过结合长短期记忆 (LSTM) 和极端梯度提升 (XGBoost) 来提高情绪检测,达到97.18%的准确性.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 自然语言处理自然语言处理.
背景情况:
- 多模式情绪分析对于理解文本,图像和音频中的情绪至关重要.
- 现有的方法在从各种数据源中捕捉微妙的情绪方面面临挑战.
研究的目的:
- 为多式联络情绪分析引入一种新的混合LXGB模型.
- 在复杂的数据集中提高情绪检测的准确性和有效性.
主要方法:
- 开发了混合LXGB模型,集成长期短期内存 (LSTM) 和极端梯度增强 (XGBoost).
- 利用深度学习和渐变增强技术进行情绪分析.
主要成果:
- 在CMU-MOSEI数据集上实现了97.18%的准确性.
- 超过了个人LSTM,CNN,DNN和XGBoost分类器的表现.
- 在捕捉微妙的情感方面表现出卓越的表现.
结论:
- 混合LXGB模型在多式联络情绪分析方面取得了重大进展.
- 集成LSTM和XGBoost提供了更好的性能和平衡.
- 该模型显示了对现实世界应用的巨大潜力.
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