校园社交网络中公众论风险的决策模型基于混合动态删除和最短路径算法
1School of Economics and Management, Xidian University, Xi'an, China.
PloS one
|November 18, 2024
概括
本研究引入了一个动态删除最短路径算法和一个注意力增强的循环神经网络,用于高效的网络文本情绪分类,提高准确性和减少公共论监测中的资源浪费.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 自然语言处理自然语言处理.
背景情况:
- 传统的网络论监测是低效的,资源密集的.
- 现有的方法在准确和快速的文本分类方面扎.
研究的目的:
- 开发一个高效的网络文本分类算法.
- 创建一个改进的文本情感分类模型.
- 加强校园社交网络意见风险决策.
主要方法:
- 一个动态删除-最短路径算法用于网络文本分类.
- 一个使用卷积神经网络和循环神经网络的变体的文本情感分类模型.
- 整合注意力机制以改进分类模型.
主要成果:
- 动态删除最短路径算法实现了97.30%的精度,79.55%的回忆率和87.53%的F值,达到397 KB/s.
- 经常性神经网络变体模型显示长文本的准确性和F值高于84%.
- 注意力机制提高了3.89%的文本情绪分类准确性.
结论:
- 拟议的动态删除-最短路径算法和注意力增强的情绪分类模型展示了卓越的性能.
- 这些进步为校园社交网络意见风险管理提供了显著的应用价值.
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