一个注意力意识长期短期记忆 - - 像尖的神经模型用于情感分析
Qian Liu1, Yanping Huang1, Qian Yang1
1School of Computer and Software Engineering, Xihua University, P. R. China.
International journal of neural systems
|June 12, 2023
概括
一个新的方面级情绪分析 (ALS) 模型使用长期短期记忆 (LSTM) 网络,其灵感来源于尖端神经P (SNP) 系统. 与17个基线模型相比,这种ALS模型表现出卓越的性能和更简单的结构.
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.
- 计算神经科学是一种神经科学.
背景情况:
- 情绪分析对于理解用户意见至关重要.
- 现有的模型在捕捉与特定方面相关的细微情绪方面面临挑战.
- 长短期记忆 (LSTM) 网络为NLP中的序列建模提供了潜力.
研究的目的:
- 引入一个新的方面级情绪分析 (ALS) 模型.
- 为了利用LSTM-SNP模型,灵感来自尖端神经P (SNP) 系统,用于增强情绪特征提取.
- 改进语境和方面词之间的相关性计算.
主要方法:
- 开发集成LSTM-SNP架构的ALS模型.
- 在LSTM-SNP中整合了三个门:重置,消耗和生成门.
- 将注意力机制与LSTM-SNP模型集成,以关注相关特征.
主要成果:
- 该ALS模型在面层情绪分析方面表现出卓越的表现.
- 对17个基线模型的比较实验在3个现实数据集上证实了有效性.
- 与现有方法相比,ALS模型具有更简单的结构设计.
结论:
- 拟议的ALS模型基于LSTM-SNP,有效地捕获情绪特征以进行方面级分析.
- 注意力机制的整合增强了模型与上下文和方面词语相关联的能力.
- 该ALS模型代表了面向层面情绪分析的重大进步.
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