电子科技2025，Vol.38Issue(5)：22-30,9.DOI:10.16180/j.cnki.issn1007-7820.2025.05.004
结合共注意网络的深度BiGRU和DPCS情感分析模型
Deep BiGRU and DPCS Sentiment Analysis Model Combined with Coattention Network
摘要
Abstract
In view of the problem of polysemous phenomena and the inability of emotion analysis model to ex-tract comprehensive deep semantic features,this paper proposes a deep BiGRU(Bidirectional Gated Recurrent Unit)and DPCS(Deep Convolutional Attention Networks)emotion analysis model combined with coattention network.The model uses RoBERTa(Robustly optimized BERT approach)to obtain dynamic semantic representation of text,ex-tracts deep contextual semantic features and important local text features through parallel dual-channel network deep BiGRU and DPCS,and uses co-attention network-based feature fusion to deeply integrate different aspects of text semantic features to obtain more comprehensive and deep global semantic features.In order to verify the validity of the proposed model,an experimental comparison is performed on the data set of movie and online shopping reviews.The experimental results show that the accuracy and F1 of the proposed model are higher than other models,and the accuracy of the two data sets reaches 93.05%and 94.67%,respectively.关键词
文本情感分析/RoBERTa/双向门控循环神经网络/自注意力机制/卷积神经网络/动态共注意力网络/特征融合/全局语义特征Key words
text sentiment analysis/RoBERTa/bidirectional gated recurrent unit/self-attention mechanism/convolutional neural network/dynamic coattention network/feature fusion/global semantic feature分类
计算机与自动化引用本文复制引用
陈漫漫,于莲芝..结合共注意网络的深度BiGRU和DPCS情感分析模型[J].电子科技,2025,38(5):22-30,9.基金项目
国家自然科学基金(61603257) National Natural Science Foundation of China(61603257) （61603257）
