关系图注意力网络的方面级情感分析模型
Aspect-level sentiment analysis model of relational graph attention network
陈万志 1刘久龙 1王天元2
作者信息
- 1. 辽宁工程技术大学 软件学院,辽宁 葫芦岛 125105
- 2. 国网辽宁省电力有限公司 营口供电公司,辽宁 营口 115002
- 折叠
摘要
针对方面级情感分析利用注意力机制和传统深度学习方法提取方面词与上下文之间的联系时,未充分考虑句法依存信息及关系标签导致预测效果不佳的问题,提出一种基于关系图注意力网络的分析模型.利用DeBERTa预训练模型进行词嵌入,并将初始词向量进行多头注意力计算以增强方面词与上下文信息之间的关系.通过图注意力网络学习句法信息中的关系标签特征,借助这些关系标签特征进一步提取句法信息中方面词和上下文之间的联系,增强模型对于情感特征的提取能力.SemEval-2014 数据集的实验测试结果表明,所提出模型的准确率和Macro-F1均优于对比模型.
Abstract
In response to the problem of aspect-level sentiment analysis uses attention mechanism and traditional deep learning methods to extract the relations between aspect words and contextual words at present,which do not fully considers syntactic dependency information and relational labels,resulting the worse the predicted effects,an relational graph attention network of aspect-level sentiment analysis was proposed.Firstly,pre-training model DeBERTa was used to get word embedding and initial word vector was used for multi-head attention to enhance the relationship between aspect words and contextual words.Then,relational labels were learned through graph attention networks.The relationship between aspects and contextual words can be further extracted with these relational label features,which improve ability of the model to extract sentimental features.Experimental results on the SemEval-2014 dataset show that the model outperforms the comparison model in both accuracy and Macro-F1.
关键词
情感分析/注意力机制/图神经网络/句法依赖树Key words
sentiment analysis/attention mechanism/graph attention network/syntactic dependency tree引用本文复制引用
基金项目
辽宁省教育厅高等学校基本科研项目(LJKZ0327)
出版年
2024
国家科技期刊平台
NSTL
万方数据