基于预先训练的BiLSTM和语法意识图注意力网络的方面类别情绪分析.
Guixian Xu1,2, Zhe Chen3,4, Zixin Zhang3,4
1Key Laboratory of Ethnic Language Intelligent Analysis and Security Governance of MOE, Minzu University of China, Beijing, 100081, China. guixian_xu@muc.edu.cn.
Scientific reports
|January 27, 2025
概括
本研究引入了一种新的方法,即使用双向长期短期记忆 (BiLSTM) 和语法感知图注意力网络进行面向类别情感分析 (ACSA). 该方法通过更好地将情感词与方面类别相匹配来提高准确性,优于现有的模型.
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
- 计算语言学 计算语言学
背景情况:
- 视角类别情绪分析 (ACSA) 旨在识别文本中对特定类别的情绪.
- 现有的ACSA方法难以准确地将情感词与方面类别联系起来,特别是当语义相关性间接时.
- 在ACSA的一个重大挑战是注释数据集的稀缺性,阻碍了模型培训和性能.
研究的目的:
- 提出一个新的,有效的方法,以分析情绪 (ACSA) 的方面类别.
- 解决现有ACSA方法在将情感词与相关方面类别相匹配方面的局限性.
- 通过使用转移学习来克服注释数据不足的挑战.
主要方法:
- 使用预先训练的双向长期短期记忆 (BiLSTM) 模型,对文档级数据集进行初始情绪分析.
- 实现了一个语法意识的图表注意力网络,以利用语法结构和语义信息进行细粒度的情绪预测.
- 员工通过将预先训练的BiLSTM参数转移到面层ACSA模型来转移学习.
主要成果:
- 与基线模型相比,拟议的方法在五个用户评论文本数据集中显示出更高的性能.
- 综合性废除实验验证实了BiLSTM和语法意识图表注意力网络方法的有效性.
- 整合预先训练的知识和语法分析显著改善了ACSA任务的完成.
结论:
- 新的ACSA方法有效地解决了将情感词与方面类别相匹配的挑战.
- 转移学习与语法意识的图表注意力网络相结合,为ACSA提供了强大的解决方案,即使注释数据有限.
- 拟议的方法代表了细粒度情绪分析的重大进步,提供了更好的准确性和可靠性.
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