融合情感词典和自注意力的双通道情感分析模型  

A Dual-Channel Sentiment Analysis Model Integrating Sentiment Lexcion and Self-Attention

作　　者：巨家骥 黄勃[1] 张帅 郭茹燕 JU Jiaji;HUANG Bo;ZHANG Shuai;GUO Ruyan(School of Electrical and Electronic Engineering,Shanghai University of Engineering and Technology,Shanghai 201620,China)

机构地区：[1]上海工程技术大学,电子电气工程学院,上海201620

出　　处：《数据与计算发展前沿》2023年第4期101-111,共11页Frontiers of Data & Computing

基　　金：国家重点研发计划(No:2020AAA0109300);上海市科委科技创新行动计划(22S31903700,21S31904200)。

摘　　要：【应用背景】针对自然语言处理中的情感分析任务,目前的深度学习方法还是通过大量的数据训练来逐步提升效果,并没有充分利用文本中的情感词信息。【方法】本文提出了一种集成了情感词典和注意力机制的双通道文本情感分析模型。基于自注意力机制的通道负责提取语义特征,基于情感注意力的通道负责提取情感特征,两个通道分别提取的特征融合后获得文本最终的向量表达。同时引入一种注意力软约束来平衡两个通道中的注意力。【结果】实验结果表明,双通道的结构能够分别关注文本的不同特征,语义特征和情感特征结合起来有效提升了模型的分类性能。由于集成了情感词典,模型还具有较好的可解释性。【结论】本文提出的情感分析模型与相关模型相比拥有较好的性能和可解释性。[Application Background]For the task of sentiment analysis in natural language processing,current deep learning methods are based on big data training to gradually improve the effect,and do not fully exploit the information of sentiment words in the text.[Methods]This paper proposes a dual-channel text sentiment analysis model that integrates sentiment lexicon and attention mechanism.The channel based on self-attention is responsible for extracting semantic features,and the channel based on emotional attention is to extract emotional features,and the features extracted by the two channels are fused to obtain the final vector representation of the text.A soft constraint of attention is also introduced to balance the attention in both channels.[Results]The experimental results show that the dual-channel structure can focus on different features of the text separately,and the combination of semantic and sentiment features significantly improves the sentiment classification performance of the model.The model also has better interpretability due to the integration of the sentiment lexicon.[Conclusions]The sentiment analysis model proposed in this paper has better performance and interpretability compared with related models.

关 键 词：深度学习 情感词典 文本情感分析 双通道 注意力 

分 类 号：TP391.1[自动化与计算机技术—计算机应用技术]