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
管理层讨论与分析(Management discussion and analysis,MD&A)文本中包含大量的隐式情感信息,无法使用情感词直接判断情感类别.提出融合句子结构特征、上下文语义特征和外部金融知识与情感信息的金融文本隐式情感识别模型:使用依存...管理层讨论与分析(Management discussion and analysis,MD&A)文本中包含大量的隐式情感信息,无法使用情感词直接判断情感类别.提出融合句子结构特征、上下文语义特征和外部金融知识与情感信息的金融文本隐式情感识别模型:使用依存句法分析将MD&A文本中的语句表示为依存句法树,基于图注意力机制获得关键语句的结构特征;使用Bi-LSTM和注意力机制获取关键上下文语义信息;使用FinBERT金融预训练模型引入金融领域知识和情感信息.在此基础上,将MD&A文本的隐式情感特征应用于企业财务困境预测任务,提升机器学习算法的预测能力.实验表明,该模型可以提升MD&A文本的隐式情感特征识别能力,融合隐式情感特征的机器学习模型可以大幅度提升上市公司财务困境预测效果.展开更多
情感分类是自然语言处理领域的热点研究问题之一,方面级的文本情感分类旨在识别文本不同方面间的情感极性。针对方面级情感分类模型存在特征提取能力弱、方面词与上下文间交互不充分的问题,提出基于交互对抗网络的方面级情感分类模型(As...情感分类是自然语言处理领域的热点研究问题之一,方面级的文本情感分类旨在识别文本不同方面间的情感极性。针对方面级情感分类模型存在特征提取能力弱、方面词与上下文间交互不充分的问题,提出基于交互对抗网络的方面级情感分类模型(Aspect-level Sentiment classification model based on Interactive Adversarial Networks,ASIAN)。首先,通过Transformer的双向表征编码器模型作为编码器,将方面词和上下文进行单独建模提取隐含层特征。其次,构建交互注意力网络,将隐含层特征进行交互学习。最后,对交互信息进行联合学习,做交叉熵损失、回传参数。此外,ASIAN添加了对抗训练旨在进一步优化分类效果。在SemEval-2014任务4中的Laptop、Restaurant数据集和ACL-2014的Twitter数据集上,ASIAN与大多数基线模型相比有较高的分类准确率。展开更多
The increasing popularity of social media in recent years has created new opportunities to study the interactions of different groups of people. Never before have so many data about such a large number of individuals ...The increasing popularity of social media in recent years has created new opportunities to study the interactions of different groups of people. Never before have so many data about such a large number of individuals been readily available for analysis. Two popular topics in the study of social networks are community detection and sentiment analysis. Community detection seeks to find groups of associated individuals within networks, and sentiment analysis attempts to determine how individuals are feeling. While these are generally treated as separate issues, this study takes an integrative approach and uses community detection output to enable community-level sentiment analysis. Community detection is performed using the Walktrap algorithm on a network of Twitter users associated with Microsoft Corporation’s @technet account. This Twitter account is one of several used by Microsoft Corporation primarily for communicating with information technology professionals. Once community detection is finished, sentiment in the tweets produced by each of the communities detected in this network is analyzed based on word sentiment scores from the well-known SentiWordNet lexicon. The combination of sentiment analysis with community detection permits multilevel exploration of sentiment information within the @technet network, and demonstrates the power of combining these two techniques.展开更多
The unique ways of information organization and dissemination was examined through the microblog and the real-name social network as the representatives of the new virtual social networks. In order to discuss the inte...The unique ways of information organization and dissemination was examined through the microblog and the real-name social network as the representatives of the new virtual social networks. In order to discuss the interrelation and interaction of the two dimensions-topic and user, a supernetwork model was established based on the supernetwork research method. Through the actual data, a supernetwork topology diagram and the changing rule of user participation were attained. And it was concluded that the key factor of dealing with emergent online public sentiment should start with affecting the opinions of key figures, whose opinions would further affect the public opinions.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.
文摘管理层讨论与分析(Management discussion and analysis,MD&A)文本中包含大量的隐式情感信息,无法使用情感词直接判断情感类别.提出融合句子结构特征、上下文语义特征和外部金融知识与情感信息的金融文本隐式情感识别模型:使用依存句法分析将MD&A文本中的语句表示为依存句法树,基于图注意力机制获得关键语句的结构特征;使用Bi-LSTM和注意力机制获取关键上下文语义信息;使用FinBERT金融预训练模型引入金融领域知识和情感信息.在此基础上,将MD&A文本的隐式情感特征应用于企业财务困境预测任务,提升机器学习算法的预测能力.实验表明,该模型可以提升MD&A文本的隐式情感特征识别能力,融合隐式情感特征的机器学习模型可以大幅度提升上市公司财务困境预测效果.
文摘情感分类是自然语言处理领域的热点研究问题之一,方面级的文本情感分类旨在识别文本不同方面间的情感极性。针对方面级情感分类模型存在特征提取能力弱、方面词与上下文间交互不充分的问题,提出基于交互对抗网络的方面级情感分类模型(Aspect-level Sentiment classification model based on Interactive Adversarial Networks,ASIAN)。首先,通过Transformer的双向表征编码器模型作为编码器,将方面词和上下文进行单独建模提取隐含层特征。其次,构建交互注意力网络,将隐含层特征进行交互学习。最后,对交互信息进行联合学习,做交叉熵损失、回传参数。此外,ASIAN添加了对抗训练旨在进一步优化分类效果。在SemEval-2014任务4中的Laptop、Restaurant数据集和ACL-2014的Twitter数据集上,ASIAN与大多数基线模型相比有较高的分类准确率。
文摘The increasing popularity of social media in recent years has created new opportunities to study the interactions of different groups of people. Never before have so many data about such a large number of individuals been readily available for analysis. Two popular topics in the study of social networks are community detection and sentiment analysis. Community detection seeks to find groups of associated individuals within networks, and sentiment analysis attempts to determine how individuals are feeling. While these are generally treated as separate issues, this study takes an integrative approach and uses community detection output to enable community-level sentiment analysis. Community detection is performed using the Walktrap algorithm on a network of Twitter users associated with Microsoft Corporation’s @technet account. This Twitter account is one of several used by Microsoft Corporation primarily for communicating with information technology professionals. Once community detection is finished, sentiment in the tweets produced by each of the communities detected in this network is analyzed based on word sentiment scores from the well-known SentiWordNet lexicon. The combination of sentiment analysis with community detection permits multilevel exploration of sentiment information within the @technet network, and demonstrates the power of combining these two techniques.
基金National Natural Science Foundation of China (No. 71071098)
文摘The unique ways of information organization and dissemination was examined through the microblog and the real-name social network as the representatives of the new virtual social networks. In order to discuss the interrelation and interaction of the two dimensions-topic and user, a supernetwork model was established based on the supernetwork research method. Through the actual data, a supernetwork topology diagram and the changing rule of user participation were attained. And it was concluded that the key factor of dealing with emergent online public sentiment should start with affecting the opinions of key figures, whose opinions would further affect the public opinions.
