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

作　　者：陈漫漫 于莲芝[1] CHEN Manman;YU Lianzhi(School of Optical-Electrical and Computer Engineering,University of Shanghai for Science and Technology,Shanghai 200093,China)

机构地区：[1]上海理工大学光电信息与计算机工程学院,上海200093

出　　处：《电子科技》2025年第5期22-30,共9页Electronic Science and Technology

基　　金：国家自然科学基金(61603257)。

摘　　要：针对一词多义现象和情感分析模型无法提取全面深度语义特征等问题,文中提出结合共注意网络的深度BiGRU(Bidirectional Gated Recurrent Unit)和DPCS(Deep Convolutional Attention Networks)情感分析模型。使用RoBERTaRoBERTa(Robustly optimized BERT approach)获取文本的动态语义表征,通过并行双通道网络的深度BiGRU与DPCS分别提取深层次文本上下文语义特征和重要文本局部特征,利用基于共注意网络的特征融合将不同方面文本语义特征进行深度融合以获取更全面深层次的全局语义特征。为验证所提模型的有效性,在电影和线上购物评论数据集上进行实验对比。实验结果表明,所提模型的准确率和F1均高于其他模型,在两个数据集上准确率分别达到了93.05%和94.67%。In view of the problem of polysemous phenomena and the inability of emotion analysis model to extract 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,extracts 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 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]