改进了图形卷积网络,用于社交媒体中情感分析的文本.
Bharti Khemani1, Shruti Patil2, Sachin Malave3
1A. P. Shah Institute of Technology, Mumbai University, Thane, India.
MethodsX
|June 10, 2025
概括
这项研究引入了一个改进的图形卷积网络 (IGCN),用于增强社交媒体文本中的情感分类. 该模型实现了高精度,改进了情绪分析和心理健康监测应用程序.
科学领域:
- 自然语言处理 (NLP) 是一种自然语言处理.
- 人工智能 (AI) 是一种人工智能.
- 计算语言学 计算语言学
背景情况:
- 了解社交媒体文本中的情绪对于诸如心理健康监测和情绪分析等应用程序至关重要.
- 现有的模型往往难以在社交媒体数据中捕捉深层次的语义关系.
研究的目的:
- 开发一个改进的图形卷积网络 (IGCN),用于在社交媒体文本中准确地分类情感.
- 增强语义关系的表示,提高模型的可解释性.
主要方法:
- 使用基于点向互联信息 (PMI) 的图形构造方法来建模单词关系.
- 集成了一个注意力机制,以强调具有上下文意义的单词.
- 将IGCN模型应用于大型数据集,包括Twitter_EA和情绪识别数据集.
主要成果:
- 在基准数据集上实现了78.64%和92.38%的分类准确度.
- 证明了图形神经网络 (GNN) 对大规模情绪分类的有效性.
- 通过注意力加权的单词重要性,展示了更好的解释性.
结论:
- 拟议的IGCN模型显著提高了社交媒体文本中情感分类的准确性.
- 基于图形的NLP模型为情感分析和理解情感色调提供了变革的潜力.
- 该模型的可扩展性确保了大型社交媒体数据集的高效处理.
相关概念视频
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