中文文本 双重注意力网络 面向层次的情绪分类
Xinjie Sun1,2, Zhifang Liu1, Hui Li1
1Institute of Computer Science, Liupanshui Normal University, Liupanshui, Guizhou, China.
PloS one
|March 7, 2024
概括
一个新的双重注意网络通过分析语法依赖性和上下文,有效地处理中文文本以进行面层情绪识别. 这种方法提高了识别情绪趋势和取意见的准确性.
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:
- 中文文本往往缺乏明确的主题,使单词依赖性分析复杂化,特别是在方面级情感识别方面.
- 现有的模型与中文情感表达的细微和上下文依赖性质作斗争.
- 准确的方面级情绪识别对于理解用户意见和反至关重要.
研究的目的:
- 提出一种新的中文文本双重注意力网络,以增强面层情绪识别.
- 为应对在中文文本中松散的句子结构和缺乏主题所带来的挑战.
- 提高中国评论和实验总结中的情感分析的准确性和效率.
主要方法:
- 开发了一种中文语法依赖性分析方法,与情感词典相结合,用于精确的方面级情感词提取.
- 采用卷积神经网络 (CNN) 和双向长期短期记忆 (BILSTM) 与位置编码来捕获上下文级特征.
- 实施了两级注意力机制,以提取细粒度的方面级情绪信息.
主要成果:
- 拟议的双重注意网络实现了0.9180,0.9080和0.8380的高准确率,超过了十个先进的基线模型.
- 实验证明了该模型在快速准确地提取情绪词语,意见和分类情绪趋势方面的有效性.
- 废弃性研究证实了拟议网络中的每个模块的重大贡献.
结论:
- 中国文本双重注意网络提供了一个强大的解决方案,用于在中文方面层面的情绪识别.
- 整合语法依赖,情感词典,CNN-BILSTM和两级注意力机制显著提高了性能.
- 这种方法为分析中国自然语言数据中的情绪提供了更有效,更准确的方法.
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