情感-RGC网:使用RoBERTa和图形神经网络在社交媒体上进行情感识别的新方法
Jiangting Yan1, Pengju Pu1, Liheng Jiang2
1School of Art & Design, Shaanxi Fashion Engineering University, Xi'an, China.
PloS one
|March 3, 2025
概括
这项研究介绍了Emotion-RGC Net,这是一种用于社交媒体情绪识别的深度学习模型. 它通过将文本分析 (RoBERTa) 与用户交互模式 (GNN) 和标签一致性 (CRF) 结合起来,显著提高了准确性.
科学领域:
- 人工智能的人工智能
- 自然语言处理自然语言处理.
- 社交媒体分析
背景情况:
- 由于非结构化用户数据,社交媒体中的情感识别是复杂的.
- 现有的模型在不同的环境中难以获得准确性和稳定性.
研究的目的:
- 提出Emotion-RGC Net,一种用于增强情绪分类的新型深度学习模型.
- 提高识别社交媒体内容情绪的准确性和稳定性.
主要方法:
- 使用RoBERTa进行文本特征提取.
- 使用图形神经网络 (GNN) 通过用户交互来建模情绪传播.
- 整合条件随机场 (CRF) 用于一致的情感标签预测.
主要成果:
- 在情感数据集上获得了89.70%的准确性140和情感数据集的88.50%.
- 与传统和基线深度学习模型相比,显示出显著的改进.
- 在粗和细粒度情绪分类中表现出有效性.
结论:
- 情感-RGC网为社交媒体情感识别提供了一个强大的解决方案.
- 未来的工作包括减少数据依赖,提高效率,并纳入时间动态.
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