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在物联网集成的元宇宙媒体沟通中,增强了受众情绪分析
Hongtao Wang1, Shan Wang1, Yijun Lu2
1College of Music and Dance, Guangzhou University, Guangzhou, China.
PloS one
|October 30, 2025
概括
本研究介绍了BG-Hybrid,这是一个用于实时情绪分析的深度学习框架,集成了Metaverse和物联网 (IoT) 数据. 它为复杂的媒体流实现了高精度和可扩展性.
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.
- 人与计算机的交互
背景情况:
- 传统的情绪分析与来自Metaverse和物联网 (IoT) 环境的高速,异质数据作斗争.
- 在跨平台媒体分析中,上下文,情感和时间动态往往会丢失.
- 需要一个可扩展的实时解决方案来处理复杂的多源数据流.
研究的目的:
- 为情绪分析提出一个深度学习框架,它可以无地集成支持物联网的设备和Metaverse媒体交互.
- 开发一种能够检测微妙情绪并实时理解上下文的模型.
- 解决处理动态,跨平台数据的传统方法的局限性.
主要方法:
- 开发了BG-Hybrid模型,结合了BERT主导的双向编码和基于GPT的生成建模.
- 实现了五个模块架构:多源数据收集 (RESTful API),加权预处理,Adam优化训练,自适应实时处理 (动态窗口细分) 和持续改进循环.
- 利用时间情绪变化的预测值和数据可靠性的异常检测.
主要成果:
- 在基准数据集 (Twitter情绪140,亚马逊评论) 上获得了94.5%的准确性和91.5%的F1分数.
- 显示了250ms的平均响应延迟,表明有效的实时处理.
- 验证了超过91.5%的系统可扩展性,证明了其处理大量数据的能力.
结论:
- BG-Hybrid模型有效地解决了在集成的Metaverse和物联网生态系统中实时情绪分析的挑战.
- 深度学习框架为细微的情绪检测和上下文分析提供了强大的解决方案.
- 该系统表现出高性能,低延迟,以及对未来应用的显著可扩展性.
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