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
The burgeoning use of Web 2.0-powered social media in recent years has inspired numerous studies on the content and composition of online social networks (OSNs). Many methods of harvesting useful information from soci...The burgeoning use of Web 2.0-powered social media in recent years has inspired numerous studies on the content and composition of online social networks (OSNs). Many methods of harvesting useful information from social networks’ immense amounts of user-generated data have been successfully applied to such real-world topics as politics and marketing, to name just a few. This study presents a novel twist on two popular techniques for studying OSNs: community detection and sentiment analysis. Using sentiment classification to enhance community detection and community partitions to permit more in-depth analysis of sentiment data, these two techniques are brought together to analyze four networks from the Twitter OSN. The Twitter networks used for this study are extracted from four accounts related to Microsoft Corporation, and together encompass more than 60,000 users and 2 million tweets collected over a period of 32 days. By combining community detection and sentiment analysis, modularity values were increased for the community partitions detected in three of the four networks studied. Furthermore, data collected during the community detection process enabled more granular, community-level sentiment analysis on a specific topic referenced by users in the dataset.展开更多
Easy accessibility and light content filtering attempt have made microblogging sites the most popular platforms for users to share their experiences and express their opinions.Extracting from the user-composed microbl...Easy accessibility and light content filtering attempt have made microblogging sites the most popular platforms for users to share their experiences and express their opinions.Extracting from the user-composed microblogs the opinions expressed are of great significance for many practical applications.However,such task is very challenging,in particular for Chinese Microblogs.A novel representation of the opinions expressed in microblog sentences is presented and a recurrent neural network(RNN) based sequence labeling approach is proposed about sentiment parsing of Chinese microblogs.The experiments evaluate the performance of different RNN models and explore the bi-directional and deep versions of each model on a Chinese microblog corpus built by this paper.Experimental results show that the bidirectional version of the gated recurrent unit(GRU) model with three layers achieves the highest F-score 0.622.展开更多
The rising popularity of online social networks (OSNs), such as Twitter, Facebook, MySpace, and LinkedIn, in recent years has sparked great interest in sentiment analysis on their data. While many methods exist for id...The rising popularity of online social networks (OSNs), such as Twitter, Facebook, MySpace, and LinkedIn, in recent years has sparked great interest in sentiment analysis on their data. While many methods exist for identifying sentiment in OSNs such as communication pattern mining and classification based on emoticon and parts of speech, the majority of them utilize a suboptimal batch mode learning approach when analyzing a large amount of real time data. As an alternative we present a stream algorithm using Modified Balanced Winnow for sentiment analysis on OSNs. Tested on three real-world network datasets, the performance of our sentiment predictions is close to that of batch learning with the ability to detect important features dynamically for sentiment analysis in data streams. These top features reveal key words important to the analysis of sentiment.展开更多
针对文本分类的深度学习主流模型中存在的特征提取不全面、位置结构信息缺失等问题,提出一种融合情感簇的混合神经网络短文本情感分类模型(sentiment clustering and fusion of multiple neural networks,SCMN)。该方法首先通过双向变...针对文本分类的深度学习主流模型中存在的特征提取不全面、位置结构信息缺失等问题,提出一种融合情感簇的混合神经网络短文本情感分类模型(sentiment clustering and fusion of multiple neural networks,SCMN)。该方法首先通过双向变换器模型(bidirectional encoder representations from Transformers,BERT)预训练模型生成词向量,并进行情感簇聚类和情感权重增强;然后使用带有注意力机制的双向长短期记忆网络(bidirectional long short term memory,BiLSTM),捕获文本的上下文特征;再通过胶囊网络(capsual network,CapsNet)提取带有句子结构信息的局部语义特征并完成分类。基于公开数据集和自爬取数据集,将本文模型与深度学习主流分类模型进行对比实验及不同组件的消融实验。实验结果表明,相较于其他方法,本文模型精确率实现了平均5.5%的增长,证实了不同组件能为模型带来有效增益,提升文本情感分类效果。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.
文摘The burgeoning use of Web 2.0-powered social media in recent years has inspired numerous studies on the content and composition of online social networks (OSNs). Many methods of harvesting useful information from social networks’ immense amounts of user-generated data have been successfully applied to such real-world topics as politics and marketing, to name just a few. This study presents a novel twist on two popular techniques for studying OSNs: community detection and sentiment analysis. Using sentiment classification to enhance community detection and community partitions to permit more in-depth analysis of sentiment data, these two techniques are brought together to analyze four networks from the Twitter OSN. The Twitter networks used for this study are extracted from four accounts related to Microsoft Corporation, and together encompass more than 60,000 users and 2 million tweets collected over a period of 32 days. By combining community detection and sentiment analysis, modularity values were increased for the community partitions detected in three of the four networks studied. Furthermore, data collected during the community detection process enabled more granular, community-level sentiment analysis on a specific topic referenced by users in the dataset.
基金National Natural Science Foundation of China(No.71331008)
文摘Easy accessibility and light content filtering attempt have made microblogging sites the most popular platforms for users to share their experiences and express their opinions.Extracting from the user-composed microblogs the opinions expressed are of great significance for many practical applications.However,such task is very challenging,in particular for Chinese Microblogs.A novel representation of the opinions expressed in microblog sentences is presented and a recurrent neural network(RNN) based sequence labeling approach is proposed about sentiment parsing of Chinese microblogs.The experiments evaluate the performance of different RNN models and explore the bi-directional and deep versions of each model on a Chinese microblog corpus built by this paper.Experimental results show that the bidirectional version of the gated recurrent unit(GRU) model with three layers achieves the highest F-score 0.622.
文摘The rising popularity of online social networks (OSNs), such as Twitter, Facebook, MySpace, and LinkedIn, in recent years has sparked great interest in sentiment analysis on their data. While many methods exist for identifying sentiment in OSNs such as communication pattern mining and classification based on emoticon and parts of speech, the majority of them utilize a suboptimal batch mode learning approach when analyzing a large amount of real time data. As an alternative we present a stream algorithm using Modified Balanced Winnow for sentiment analysis on OSNs. Tested on three real-world network datasets, the performance of our sentiment predictions is close to that of batch learning with the ability to detect important features dynamically for sentiment analysis in data streams. These top features reveal key words important to the analysis of sentiment.
文摘针对文本分类的深度学习主流模型中存在的特征提取不全面、位置结构信息缺失等问题,提出一种融合情感簇的混合神经网络短文本情感分类模型(sentiment clustering and fusion of multiple neural networks,SCMN)。该方法首先通过双向变换器模型(bidirectional encoder representations from Transformers,BERT)预训练模型生成词向量,并进行情感簇聚类和情感权重增强;然后使用带有注意力机制的双向长短期记忆网络(bidirectional long short term memory,BiLSTM),捕获文本的上下文特征;再通过胶囊网络(capsual network,CapsNet)提取带有句子结构信息的局部语义特征并完成分类。基于公开数据集和自爬取数据集,将本文模型与深度学习主流分类模型进行对比实验及不同组件的消融实验。实验结果表明,相较于其他方法,本文模型精确率实现了平均5.5%的增长,证实了不同组件能为模型带来有效增益,提升文本情感分类效果。
