在跨文化沟通障碍的背景下,基于BERT-CNN-BiGRU-attention模型的教学情感分析
Xin Bi1, Tian Zhang2
1School of Literature, Heilongjiang University, Harbin, Heilongjiang, China.
PeerJ. Computer science
|July 10, 2024
概括
本研究介绍了BCBA模型,用于在人与计算机对话中改进语言情感识别. 混合模型提高了情感分析的准确性,这对于跨文化沟通和用户体验至关重要.
科学领域:
- 自然语言处理自然语言处理.
- 人工智能的人工智能
- 人与计算机的交互
背景情况:
- 全球化增加了跨文化沟通的挑战.
- 人与计算机的对话是应对这些挑战的关键领域.
- 准确的情绪识别对于有效的人机交互至关重要.
研究的目的:
- 在人与计算机对话中增强语言情感识别能力.
- 开发一个强大的模型来识别用户的情绪倾向.
- 提高人机交互的效率和用户体验.
主要方法:
- 提出了一个混合模型 (BCBA),集成BERT,CNN,BIGRU和注意力机制.
- 用BERT进行语义和语法特征提取.
- 采用CNN和BiGRU进行深入的文本特征分析,并注意文字权重.
主要成果:
- 在两个数据集的情绪识别和分类任务中实现了高性能.
- 显示平均准确度为0.84和平均F1得分为0.8.8.
- 展示了最小的分类错误率和约0.7.7的稳定召回率.
结论:
- BCBA模型在语义理解和情感分析方面提供了显著的改进.
- 该模型有效地处理语言中的情感特征,特别是在跨文化背景下.
- 为智能人机交互系统提供技术支持.
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