智能情绪感知使用BERT BiLSTM和生成AI进行主动客户服务
1Assistant Professor, Department of Computer Science and Engineering- Cybersecurity, Easwari Engineering College, Bharathi Salai, Ramapuram, 600089, Chennai, India. sandrakarunya@gmail.com.
Scientific reports
|October 1, 2025
概括
这项研究介绍了一种混合人工智能模型,将BERT和Bi-LSTM结合起来,用于客户服务聊天中的实时情绪检测. 这种先进的系统通过启用同情和高效的人工智能驱动的支持来改善客户体验.
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.
- 机器学习 机器学习
背景情况:
- 联系中心越来越多地使用AI聊天机器人进行客户交互.
- 目前的情感识别方法在准确性和细微差别方面存在局限性.
- 实时情绪检测对于有效的客户支持至关重要.
研究的目的:
- 开发一种新的混合人工智能模型,用于在客户服务聊天中准确,实时检测情绪.
- 将情绪检测与生成AI集成在一起,用于上下文感知响应生成.
- 增强人工智能在理解和响应客户情绪方面的能力.
主要方法:
- 使用混合模型结合BERT (变压器的双向编码器表示) 和Bi-LSTM (双向长短期内存) 网络.
- 实现了客户信息的实时处理,用于情绪分类.
- 集成了一个生成AI模块,用于起草代理响应.
主要成果:
- 与现有方法相比,混合BERT + Bi-LSTM分类器在情绪和攻击性检测方面取得了卓越的性能.
- 该系统展示了200毫秒以下的端到端延迟,用于情绪检测和响应生成.
- 使用真实世界的联系中心数据进行验证,在情绪识别中显示了增强的准确性和细微差别.
结论:
- 拟议的BERT-Bi-LSTM和生成AI集成为客户服务系统提供了显著的进步.
- 这项技术可以实现更富同情,智能和主动的客户互动.
- 导致改善客户体验,减少代理人倦怠,提高运营效率.
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