SentXFormer:一个转换器增强的混合深度学习框架,用于对客户评论的跨域情绪分析
Ajeet Kumar1, Kumar Abhishek1, Ahamed Shafeeq B M2
1Department of Computer Science and Engineering, NIT Patna, Patna, Bihar, 800005, India.
Scientific reports
|December 24, 2025
概括
深度学习模型SentXFormer通过使用混合变压器架构来改进跨域情绪分析. 它在不同领域的情绪分类方面实现了高准确性,优于现有模型.
科学领域:
- 自然语言处理自然语言处理.
- 机器学习 机器学习
- 人工智能的人工智能
背景情况:
- 跨领域情绪分析面临的挑战是由于词汇的变化,上下文的变化和特定领域的语言.
- 传统模型在不同领域的概括性方面扎,导致精度降低和性能不可靠.
研究的目的:
- 介绍SentXFormer,一种基于变压器的新型混合深度学习框架,旨在增强跨异质领域的情绪分类.
- 解决跨领域情绪分析中传统模型的局限性.
主要方法:
- SentXFormer集成了混合的SentiConGRU-Net架构 (CNN和GRU层) 与BERT,RoBERTa和域自适应BERT (DABERT) 的上下文嵌入.
- 一个使用对抗训练的域适应模块与梯度反转层 (GRL) 促进了域不变表示的学习.
- 实验对来自亚马逊,Yelp和IMDB数据集的23,440条标注情绪的评论进行了实验.
主要成果:
- SentXFormer实现了高域内准确度:98.7% (亚马逊),97.67% (耶尔普) 和98.8% (IMDB).
- 该模型表现出稳定的跨领域可转移性,在各种火车测试组合中达到91-93%的准确性.
- 对比分析证实SentXFormer的表现始终优于LSTM,CNN,GRU和其他基于变压器的适应方法等基线模型.
结论:
- SentXFormer被证明是一个高效的,强大的,可扩展的解决方案,用于在异质和现实世界的客户评论背景下进行情感分析.
- 拟议的模型有效地克服了在情绪分类任务中领域转移的挑战.
关键词:
贝尔特 (BERT) 公司美国有线电视新闻网-GRU跨领域的跨领域.客户评论 客户评论 客户评论达伯特 (Dabbert) 是一个著名的作家.域名适应领域适应总体上,GRL是指GRL是指GRL.混合深度学习是一种混合深度学习.情绪分析是一种情绪分析.变压器变压器变压器更多相关视频
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