图形卷积网络与强化依赖图和刺言论检测的拒绝机制
Pingping Yan1,2,3,4, Tianbo An5,6,7,8, Jiaxuan Yu9
1College of Computer Science and Technology, Changchun University, Changchun, 130022, China.
Scientific reports
|October 8, 2025
概括
本研究引入了一种新的图形卷积网络框架,以改善社交媒体上的刺言论检测. 该模型有效地处理依赖异质性,并抑制噪音,在情绪分析中实现卓越的性能.
科学领域:
- 自然语言处理自然语言处理.
- 计算语言学 计算语言学
- 人工智能的人工智能
背景情况:
- 刺言论的检测对于社交媒体情绪分析和公众论监测至关重要.
- 基于图形的方法显示出有希望的结果,但在同质的依赖关系和噪声聚合方面存在困难.
- 现有的方法往往无法有效地模拟语法和情感细微差别.
研究的目的:
- 提出一种新的图形卷积网络 (GCN) 框架,用于增强刺言论的检测.
- 通过建模依赖异质性和抑制噪音来解决现有的基于图形的方法的局限性.
- 提高在社交媒体环境中刺言论检测的准确性和稳定性.
主要方法:
- 构建依赖图,考虑节点距离和依赖类型.
- 使用无噪声自动编码器来实现强大的特征表示和降低噪声.
- 应用参数化机制来惩罚稀疏的边缘和抑制无关信息.
主要成果:
- 在三个不同的数据集上获得了84.97%,71.11%和83.83%的F1分数.
- 拟议的GCN模型在所有评估指标中显著超过了基线模型.
- 在处理语法和情感信息以检测刺言论方面表现出卓越的表现.
结论:
- 新的GCN框架有效地模拟依赖异质性,并抑制噪音,以改善刺言论的检测.
- 拟议的方法为社交媒体情绪分析的挑战提供了强有力的解决方案.
- 该模型的卓越表现突显了其在意见监测中的实际应用潜力.
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