通过深度学习技术对学生反的情感分析,注意力意识与堆叠嵌入
Shanza Zafar Malik1, Khalid Iqbal1, Muhammad Sharif1
1Department of Computer Science, COMSATS University Islamabad, Attock Campus, Attock, Punjab, Pakistan.
PeerJ. Computer science
|September 24, 2024
概括
这项研究引入了一种用于情感分析的新型混合模型,在分类学生反方面获得96%的F1分数. 先进的人工智能方法增强了自动极性预测,以更好地理解用户情绪.
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.
背景情况:
- 情绪分析和极性预测对于从公共数据中了解用户意见至关重要.
- 现有的方法在准确评估学生反和社交媒体评论中的情绪方面面临挑战.
- 文本嵌入和深度学习模型具有潜力,但对于复杂的NLP任务需要改进.
研究的目的:
- 解决自动极性预测和学生反情绪分析方面的挑战.
- 提出和评估一种创新的混合模型,用于增强情绪分析.
- 将拟议模型的性能与现有的最先进的深度学习技术进行比较.
主要方法:
- 开发了一个混合模型,集成集体学习的基于文本嵌入.
- 整合了多头注意力机制,以改善特征提取.
- 利用深度学习分类器的组合来进行情感分类.
主要成果:
- 拟议的混合模型实现了95%的准确性,97%的回忆,95%的精度和96%的F1得分.
- 与现有的最先进的基于深度学习的情绪分析方法相比,表现出更好的表现.
- 有效地分析学生反中的情绪,展示了高预测能力.
结论:
- 这种新的混合模型显著提升了NLP中的情绪分析能力.
- 该模型的高性能指标验证了其在分析学生反和用户情绪方面的有效性.
- 这种方法为需要准确的极性预测和情绪分类的应用提供了强大的解决方案.
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