Aspect-oriented sentiment analysis is a meticulous sentiment analysis task that aims to analyse the sentiment polarity of specific aspects. Most of the current research builds graph convolutional networks based on dep...Aspect-oriented sentiment analysis is a meticulous sentiment analysis task that aims to analyse the sentiment polarity of specific aspects. Most of the current research builds graph convolutional networks based on dependent syntactic trees, which improves the classification performance of the models to some extent. However, the technical limitations of dependent syntactic trees can introduce considerable noise into the model. Meanwhile, it is difficult for a single graph convolutional network to aggregate both semantic and syntactic structural information of nodes, which affects the final sentence classification. To cope with the above problems, this paper proposes a bi-channel graph convolutional network model. The model introduces a phrase structure tree and transforms it into a hierarchical phrase matrix. The adjacency matrix of the dependent syntactic tree and the hierarchical phrase matrix are combined as the initial matrix of the graph convolutional network to enhance the syntactic information. The semantic information feature representations of the sentences are obtained by the graph convolutional network with a multi-head attention mechanism and fused to achieve complementary learning of dual-channel features. Experimental results show that the model performs well and improves the accuracy of sentiment classification on three public benchmark datasets, namely Rest14, Lap14 and Twitter.展开更多
In recent years,deep learning methods have developed rapidly and found application in many fields,including natural language processing.In the field of aspect-level sentiment analysis,deep learning methods can also gr...In recent years,deep learning methods have developed rapidly and found application in many fields,including natural language processing.In the field of aspect-level sentiment analysis,deep learning methods can also greatly improve the performance of models.However,previous studies did not take into account the relationship between user feature extraction and contextual terms.To address this issue,we use data feature extraction and deep learning combined to develop an aspect-level sentiment analysis method.To be specific,we design user comment feature extraction(UCFE)to distill salient features from users’historical comments and transform them into representative user feature vectors.Then,the aspect-sentence graph convolutional neural network(ASGCN)is used to incorporate innovative techniques for calculating adjacency matrices;meanwhile,ASGCN emphasizes capturing nuanced semantics within relationships among aspect words and syntactic dependency types.Afterward,three embedding methods are devised to embed the user feature vector into the ASGCN model.The empirical validations verify the effectiveness of these models,consistently surpassing conventional benchmarks and reaffirming the indispensable role of deep learning in advancing sentiment analysis methodologies.展开更多
Aiming at the problem that existing models in aspect-level sentiment analysis cannot fully and effectively utilize sentence semantic and syntactic structure information, this paper proposes a graph neural network-base...Aiming at the problem that existing models in aspect-level sentiment analysis cannot fully and effectively utilize sentence semantic and syntactic structure information, this paper proposes a graph neural network-based aspect-level sentiment classification model. Self-attention, aspectual word multi-head attention and dependent syntactic relations are fused and the node representations are enhanced with graph convolutional networks to enable the model to fully learn the global semantic and syntactic structural information of sentences. Experimental results show that the model performs well on three public benchmark datasets Rest14, Lap14, and Twitter, improving the accuracy of sentiment classification.展开更多
针对现有方面级情感分类模型存在方面词与上下文交互不充分、分类精度低的问题,提出一种基于多交互特征融合的方面级情感分类方法(ASMFF:Aspect-level Sentiment classification method based on Multi-interaction Feature Fusion)。首...针对现有方面级情感分类模型存在方面词与上下文交互不充分、分类精度低的问题,提出一种基于多交互特征融合的方面级情感分类方法(ASMFF:Aspect-level Sentiment classification method based on Multi-interaction Feature Fusion)。首先,将上下文和方面词分别进行特殊标记,输入BERT(Bidirectional Encoder Representations from Transformers)编码层进行文本特征向量提取。其次,将文本特征向量输入AOA(Attention Over Attention)和IAN(Interactive Attention Networks)网络提取交互注意力特征向量。最后,将得到的两种交互特征向量进行融合学习,通过交叉熵损失函数进行概率计算、损失回传和参数更新。在Laptop、Restaurant和Twitter 3个公开数据集上的实验结果表明,ASMFF模型的分类准确率分别为80.25%、84.38%、75.29%,相比基线模型有显著提升。展开更多
文摘Aspect-oriented sentiment analysis is a meticulous sentiment analysis task that aims to analyse the sentiment polarity of specific aspects. Most of the current research builds graph convolutional networks based on dependent syntactic trees, which improves the classification performance of the models to some extent. However, the technical limitations of dependent syntactic trees can introduce considerable noise into the model. Meanwhile, it is difficult for a single graph convolutional network to aggregate both semantic and syntactic structural information of nodes, which affects the final sentence classification. To cope with the above problems, this paper proposes a bi-channel graph convolutional network model. The model introduces a phrase structure tree and transforms it into a hierarchical phrase matrix. The adjacency matrix of the dependent syntactic tree and the hierarchical phrase matrix are combined as the initial matrix of the graph convolutional network to enhance the syntactic information. The semantic information feature representations of the sentences are obtained by the graph convolutional network with a multi-head attention mechanism and fused to achieve complementary learning of dual-channel features. Experimental results show that the model performs well and improves the accuracy of sentiment classification on three public benchmark datasets, namely Rest14, Lap14 and Twitter.
基金This work is partly supported by the Fundamental Research Funds for the Central Universities(CUC230A013)It is partly supported by Natural Science Foundation of Beijing Municipality(No.4222038)It is also supported by National Natural Science Foundation of China(Grant No.62176240).
文摘In recent years,deep learning methods have developed rapidly and found application in many fields,including natural language processing.In the field of aspect-level sentiment analysis,deep learning methods can also greatly improve the performance of models.However,previous studies did not take into account the relationship between user feature extraction and contextual terms.To address this issue,we use data feature extraction and deep learning combined to develop an aspect-level sentiment analysis method.To be specific,we design user comment feature extraction(UCFE)to distill salient features from users’historical comments and transform them into representative user feature vectors.Then,the aspect-sentence graph convolutional neural network(ASGCN)is used to incorporate innovative techniques for calculating adjacency matrices;meanwhile,ASGCN emphasizes capturing nuanced semantics within relationships among aspect words and syntactic dependency types.Afterward,three embedding methods are devised to embed the user feature vector into the ASGCN model.The empirical validations verify the effectiveness of these models,consistently surpassing conventional benchmarks and reaffirming the indispensable role of deep learning in advancing sentiment analysis methodologies.
文摘Aiming at the problem that existing models in aspect-level sentiment analysis cannot fully and effectively utilize sentence semantic and syntactic structure information, this paper proposes a graph neural network-based aspect-level sentiment classification model. Self-attention, aspectual word multi-head attention and dependent syntactic relations are fused and the node representations are enhanced with graph convolutional networks to enable the model to fully learn the global semantic and syntactic structural information of sentences. Experimental results show that the model performs well on three public benchmark datasets Rest14, Lap14, and Twitter, improving the accuracy of sentiment classification.
文摘针对现有方面级情感分类模型存在方面词与上下文交互不充分、分类精度低的问题,提出一种基于多交互特征融合的方面级情感分类方法(ASMFF:Aspect-level Sentiment classification method based on Multi-interaction Feature Fusion)。首先,将上下文和方面词分别进行特殊标记,输入BERT(Bidirectional Encoder Representations from Transformers)编码层进行文本特征向量提取。其次,将文本特征向量输入AOA(Attention Over Attention)和IAN(Interactive Attention Networks)网络提取交互注意力特征向量。最后,将得到的两种交互特征向量进行融合学习,通过交叉熵损失函数进行概率计算、损失回传和参数更新。在Laptop、Restaurant和Twitter 3个公开数据集上的实验结果表明,ASMFF模型的分类准确率分别为80.25%、84.38%、75.29%,相比基线模型有显著提升。
