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
Pre-trained language models(PLMs),such as BERT,have achieved good results on many natural language processing(NLP)tasks.Recently,some studies have attempted to integrate factual knowledge into PLMs to adapt to various...Pre-trained language models(PLMs),such as BERT,have achieved good results on many natural language processing(NLP)tasks.Recently,some studies have attempted to integrate factual knowledge into PLMs to adapt to various downstream tasks.For sentiment analysis tasks,sentiment knowledge,such as sentiment words,plays a significant role in determining the sentiment tendencies of texts.For Chinese sentiment analysis,historical stories and fables imbue words with richer connotations and more complex sentiments than those typically found in English,which makes sentiment knowledge injection necessary.But clearly,this knowledge has not been fully considered.In this paper,we propose EKBSA,a Chinese sentiment analysis model,which is based on the K-BERT model and utilizes a sentiment knowledge graph to achieve better results on sentiment analysis tasks.To construct a high-quality sentiment knowledge graph,we collect a large number of sentiment words by combining several existing sentiment lexica.Moreover,in order to understand texts better,we enhance local attention through syntactic analysis and direct to EKBSA focus more on syntactically relevant words.EKBSA is compatible with BERT and existing structural knowledge.Experimental results show that EKBSA achieves better performance on Chinese sentiment analysis tasks.Built upon EKBSA,we further change the general attention to the context attention and propose Context EKBSA,so that the model can adapt to sentiment analysis tasks in Chinese conversations and achieve good performance.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supported by the National Natural Science Foundation of China under Grant Nos.62172086 and 62272092.
文摘Pre-trained language models(PLMs),such as BERT,have achieved good results on many natural language processing(NLP)tasks.Recently,some studies have attempted to integrate factual knowledge into PLMs to adapt to various downstream tasks.For sentiment analysis tasks,sentiment knowledge,such as sentiment words,plays a significant role in determining the sentiment tendencies of texts.For Chinese sentiment analysis,historical stories and fables imbue words with richer connotations and more complex sentiments than those typically found in English,which makes sentiment knowledge injection necessary.But clearly,this knowledge has not been fully considered.In this paper,we propose EKBSA,a Chinese sentiment analysis model,which is based on the K-BERT model and utilizes a sentiment knowledge graph to achieve better results on sentiment analysis tasks.To construct a high-quality sentiment knowledge graph,we collect a large number of sentiment words by combining several existing sentiment lexica.Moreover,in order to understand texts better,we enhance local attention through syntactic analysis and direct to EKBSA focus more on syntactically relevant words.EKBSA is compatible with BERT and existing structural knowledge.Experimental results show that EKBSA achieves better performance on Chinese sentiment analysis tasks.Built upon EKBSA,we further change the general attention to the context attention and propose Context EKBSA,so that the model can adapt to sentiment analysis tasks in Chinese conversations and achieve good performance.
