基于bigura的新媒体实时情绪分析
1Guangzhou Huashang College, Guangzhou, Guangdong, China.
PeerJ. Computer science
|December 13, 2024
概括
深度学习模型,如多层BiGura,擅长从新媒体数据中实时分析情绪. 这种先进的技术准确地捕捉到公众论的变化,优于传统方法.
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:
- 互联网和智能手机的普及产生了大量的新媒体数据,压倒了传统的机器学习.
- 公众论挖掘至关重要,但受到数据量和复杂性的挑战.
研究的目的:
- 提出基于深度学习的技术,用于实时情绪检测和公众论监测.
- 评估BiGura多层模型对分析文本和视频内容中的情绪的有效性.
主要方法:
- 开发了一个多层BiGura模型,用于实时情绪检测.
- 该模型在病毒事件上进行了测试,包括加沙移民场景.
- 性能与贝叶斯式和K-近邻 (KNN) 分类器进行了比较.
主要成果:
- 该模型实现了高精度:92.7%的文字情感和86.9%的视频情感.
- 深度学习显著提高了分类精度,比贝叶斯分类精度提高了3.88%,比KNN分类精度提高了4.33%.
- 该系统展示了有效的实时情绪监测和公众论趋势跟踪.
结论:
- 多层BiGura模型为新媒体情绪分析提供了强大的解决方案.
- 这项研究为准确,实时的论监测提供了先进的工具.
- 有效的论监测对社会稳定和公共安全有重大影响.
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