一个混合深度学习情绪分类系统,使用多模式数据
Dong-Hwi Kim1, Woo-Hyeok Son1, Sung-Shin Kwak1
1Department of Computer Science, Dankook University, 152 Jukjeon-ro Campus, Suji-gu, Yongin-si 16890, Republic of Korea.
Sensors (Basel, Switzerland)
|December 9, 2023
概括
这项研究引入了韩语的混合深度学习情绪分类系统 (HDECS),其性能优于现有模型. HDECS有效地处理多式联网数据,以改善各种应用中的情绪识别.
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.
- 语言 情感 识别 语言
背景情况:
- 情绪分类对于人工智能和个性化服务至关重要.
- 现有的单模态方法面临着语调,文本结构和生理信号变化的挑战.
- 韩语语言提出了独特的NLP挑战,比如主题省略和间距.
研究的目的:
- 提出一种混合多式联络深度学习系统 (HDECS),用于增强韩语情感分类.
- 解决单模态情绪分析在口语环境中的局限性.
- 使用多式联络数据来提高情绪识别准确度.
主要方法:
- 为多式联运韩国数据重新训练LSTM和CNN模型,直到预测协议超过0.75.
- 从预测生成情感句子来增强脚本数据.
- 在增强数据集上使用BERT进行最终情绪预测.
主要成果:
- 与KLUE/roBERTa模型相比,HDECS案例模型显示出更高的性能.
- 在分级交叉度 (CCE) 中取得了0.5的改进,在准确度中获得了0.09,在F1得分中获得了0.11.
- 该模型有效地整合了多式联络信息,以进行强大的情绪分类.
结论:
- 拟议的HDECS在韩国情感分类方面取得了重大进展.
- 混合方法可以适应各种语言和区域语音特征的情感分类.
- HDECS对需要细微的情感理解的现实应用具有前景.
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