微博情感分析是社会媒体挖掘中的重要任务之一,在恐怖组织识别、个性化推荐、舆情分析等方面具有重要的理论和应用价值.但与传统文本数据不同,微博消息短小而凌乱,包含着大量诸如微博表情符号之类的特有信息,同时微博情感是与其讨论主...微博情感分析是社会媒体挖掘中的重要任务之一,在恐怖组织识别、个性化推荐、舆情分析等方面具有重要的理论和应用价值.但与传统文本数据不同,微博消息短小而凌乱,包含着大量诸如微博表情符号之类的特有信息,同时微博情感是与其讨论主题是密切相关的.多数现有的微博情感分析方法都没有将微博主题与微博情感进行协同分析,或者在微博主题情感分析过程中没有考虑将用户关系、用户性格情绪等特征数据,从而导致微博情感分析与主题检测的效果难尽人意.为此,提出了一个基于多特征融合的微博主题情感挖掘模型TSMMF(Topic Sentiment Model based on Multi-feature Fusion),该模型将情感表情符号与微博用户性格情绪特征纳入到图模型LDA中实现微博主题与情感的同步推导.实验结果表明,与当前用于短文本情感主题挖掘的最优模型(JST,SLDA与DPLDA)相比较,TSMMF具有更优的微博主题情感检测性能.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
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 emergence of new media in various fields has continuously strengthened the social aspect of social media.Netizens tend to express emotions in social interactions,and many people even use satire,metaphors,and other...The emergence of new media in various fields has continuously strengthened the social aspect of social media.Netizens tend to express emotions in social interactions,and many people even use satire,metaphors,and other techniques to express some negative emotions,it is necessary to detect sarcasm in social comment data.For sarcasm,the more reference data modalities used,the better the experimental effect.This paper conducts research on sarcasm detection technology based on image-text fusion data.To effectively utilize the features of each modality,a feature reconstruction output algorithm is proposed.This algorithm is based on the attention mechanism,learns the low-rank features of another modality through cross-modality,the eigenvectors are reconstructed for the corresponding modality through weighted averaging.When only the image modality in the dataset is used,the preprocessed data has outstanding performance in reconstructing the output model,with an accuracy rate of 87.6%.When using only the text modality data in the dataset,the reconstructed output model is optimal,with an accuracy rate of 85.2%.To improve feature fusion between modalities for effective classification,a weight adaptive learning algorithm is used.This algorithm uses a neural network combined with an attention mechanism to calculate the attention weight of each modality to achieve weight adaptive learning purposes,with an accuracy rate of 87.9%.Extensive experiments on a benchmark dataset demonstrate the superiority of our proposed model.展开更多
Sentiment analysis(SA)is the procedure of recognizing the emotions related to the data that exist in social networking.The existence of sarcasm in tex-tual data is a major challenge in the efficiency of the SA.Earlier...Sentiment analysis(SA)is the procedure of recognizing the emotions related to the data that exist in social networking.The existence of sarcasm in tex-tual data is a major challenge in the efficiency of the SA.Earlier works on sarcasm detection on text utilize lexical as well as pragmatic cues namely interjection,punctuations,and sentiment shift that are vital indicators of sarcasm.With the advent of deep-learning,recent works,leveraging neural networks in learning lexical and contextual features,removing the need for handcrafted feature.In this aspect,this study designs a deep learning with natural language processing enabled SA(DLNLP-SA)technique for sarcasm classification.The proposed DLNLP-SA technique aims to detect and classify the occurrence of sarcasm in the input data.Besides,the DLNLP-SA technique holds various sub-processes namely preprocessing,feature vector conversion,and classification.Initially,the pre-processing is performed in diverse ways such as single character removal,multi-spaces removal,URL removal,stopword removal,and tokenization.Secondly,the transformation of feature vectors takes place using the N-gram feature vector technique.Finally,mayfly optimization(MFO)with multi-head self-attention based gated recurrent unit(MHSA-GRU)model is employed for the detection and classification of sarcasm.To verify the enhanced outcomes of the DLNLP-SA model,a comprehensive experimental investigation is performed on the News Headlines Dataset from Kaggle Repository and the results signified the supremacy over the existing approaches.展开更多
Online reviews are considered of an important indicator for users to decide on the activity they wish to do, whether it is watching a movie, going to a restaurant, or buying a product. It also serves businesses as it ...Online reviews are considered of an important indicator for users to decide on the activity they wish to do, whether it is watching a movie, going to a restaurant, or buying a product. It also serves businesses as it keeps tracking user feedback. The sheer volume of online reviews makes it difficult for a human to process and extract all significant information to make purchasing choices. As a result, there has been a trend toward systems that can automatically summarize opinions from a set of reviews. In this paper, we present a hybrid algorithm that combines an auto-summarization algorithm with a sentiment analysis (SA) algorithm, to offer a personalized user experiences and to solve the semantic-pragmatic gap. The algorithm consists of six steps that start with the original text document and generate a summary of that text by choosing the N most relevant sentences in the text. The tagged texts are then processed and then passed to a Naive Bayesian classifier along with their tags as training data. The raw data used in this paper belong to the tagged corpus positive and negative processed movie reviews introduced in [1]. The measures that are used to gauge the performance of the SA and classification algorithm for all test cases consist of accuracy, recall, and precision. We describe in details both the aspect of extraction and sentiment detection modules of our system.展开更多
Aspect category detection is one challenging subtask of aspect based sentiment analysis, which categorizes a review sentence into a set of predefined aspect categories. Most existing methods regard the aspect category...Aspect category detection is one challenging subtask of aspect based sentiment analysis, which categorizes a review sentence into a set of predefined aspect categories. Most existing methods regard the aspect category detection as a flat classification problem. However, aspect categories are inter-related, and they are usually organized with a hierarchical tree structure. To leverage the structure information, this paper proposes a hierarchical multi-label classification model to detect aspect categories and uses a graph enhanced transformer network to integrate label dependency information into prediction features. Experiments have been conducted on four widely-used benchmark datasets, showing that the proposed model outperforms all strong baselines.展开更多
文摘微博情感分析是社会媒体挖掘中的重要任务之一,在恐怖组织识别、个性化推荐、舆情分析等方面具有重要的理论和应用价值.但与传统文本数据不同,微博消息短小而凌乱,包含着大量诸如微博表情符号之类的特有信息,同时微博情感是与其讨论主题是密切相关的.多数现有的微博情感分析方法都没有将微博主题与微博情感进行协同分析,或者在微博主题情感分析过程中没有考虑将用户关系、用户性格情绪等特征数据,从而导致微博情感分析与主题检测的效果难尽人意.为此,提出了一个基于多特征融合的微博主题情感挖掘模型TSMMF(Topic Sentiment Model based on Multi-feature Fusion),该模型将情感表情符号与微博用户性格情绪特征纳入到图模型LDA中实现微博主题与情感的同步推导.实验结果表明,与当前用于短文本情感主题挖掘的最优模型(JST,SLDA与DPLDA)相比较,TSMMF具有更优的微博主题情感检测性能.
基金国家自然科学基金青年基金(62006212)中国博士后科学基金(2023M733907)+1 种基金信息物理社会可信服务计算教育部重点实验室开放基金(CPSDSC202103)Project of Strategic Importance Grant of the Hong Kong Polytechnic University(1-ZE2Q)资助。
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.
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funded by National Key Research and Development Program of China(No.2022YFC3302103).
文摘The emergence of new media in various fields has continuously strengthened the social aspect of social media.Netizens tend to express emotions in social interactions,and many people even use satire,metaphors,and other techniques to express some negative emotions,it is necessary to detect sarcasm in social comment data.For sarcasm,the more reference data modalities used,the better the experimental effect.This paper conducts research on sarcasm detection technology based on image-text fusion data.To effectively utilize the features of each modality,a feature reconstruction output algorithm is proposed.This algorithm is based on the attention mechanism,learns the low-rank features of another modality through cross-modality,the eigenvectors are reconstructed for the corresponding modality through weighted averaging.When only the image modality in the dataset is used,the preprocessed data has outstanding performance in reconstructing the output model,with an accuracy rate of 87.6%.When using only the text modality data in the dataset,the reconstructed output model is optimal,with an accuracy rate of 85.2%.To improve feature fusion between modalities for effective classification,a weight adaptive learning algorithm is used.This algorithm uses a neural network combined with an attention mechanism to calculate the attention weight of each modality to achieve weight adaptive learning purposes,with an accuracy rate of 87.9%.Extensive experiments on a benchmark dataset demonstrate the superiority of our proposed model.
基金supported through the Annual Funding track by the Deanship of Scientific Research,Vice Presidency for Graduate Studies and Scientific Research,King Faisal University,Saudi Arabia[Project No.AN000685].
文摘Sentiment analysis(SA)is the procedure of recognizing the emotions related to the data that exist in social networking.The existence of sarcasm in tex-tual data is a major challenge in the efficiency of the SA.Earlier works on sarcasm detection on text utilize lexical as well as pragmatic cues namely interjection,punctuations,and sentiment shift that are vital indicators of sarcasm.With the advent of deep-learning,recent works,leveraging neural networks in learning lexical and contextual features,removing the need for handcrafted feature.In this aspect,this study designs a deep learning with natural language processing enabled SA(DLNLP-SA)technique for sarcasm classification.The proposed DLNLP-SA technique aims to detect and classify the occurrence of sarcasm in the input data.Besides,the DLNLP-SA technique holds various sub-processes namely preprocessing,feature vector conversion,and classification.Initially,the pre-processing is performed in diverse ways such as single character removal,multi-spaces removal,URL removal,stopword removal,and tokenization.Secondly,the transformation of feature vectors takes place using the N-gram feature vector technique.Finally,mayfly optimization(MFO)with multi-head self-attention based gated recurrent unit(MHSA-GRU)model is employed for the detection and classification of sarcasm.To verify the enhanced outcomes of the DLNLP-SA model,a comprehensive experimental investigation is performed on the News Headlines Dataset from Kaggle Repository and the results signified the supremacy over the existing approaches.
文摘Online reviews are considered of an important indicator for users to decide on the activity they wish to do, whether it is watching a movie, going to a restaurant, or buying a product. It also serves businesses as it keeps tracking user feedback. The sheer volume of online reviews makes it difficult for a human to process and extract all significant information to make purchasing choices. As a result, there has been a trend toward systems that can automatically summarize opinions from a set of reviews. In this paper, we present a hybrid algorithm that combines an auto-summarization algorithm with a sentiment analysis (SA) algorithm, to offer a personalized user experiences and to solve the semantic-pragmatic gap. The algorithm consists of six steps that start with the original text document and generate a summary of that text by choosing the N most relevant sentences in the text. The tagged texts are then processed and then passed to a Naive Bayesian classifier along with their tags as training data. The raw data used in this paper belong to the tagged corpus positive and negative processed movie reviews introduced in [1]. The measures that are used to gauge the performance of the SA and classification algorithm for all test cases consist of accuracy, recall, and precision. We describe in details both the aspect of extraction and sentiment detection modules of our system.
基金supported by the National Natural Science Foundation of China under Grant No.62036001.
文摘Aspect category detection is one challenging subtask of aspect based sentiment analysis, which categorizes a review sentence into a set of predefined aspect categories. Most existing methods regard the aspect category detection as a flat classification problem. However, aspect categories are inter-related, and they are usually organized with a hierarchical tree structure. To leverage the structure information, this paper proposes a hierarchical multi-label classification model to detect aspect categories and uses a graph enhanced transformer network to integrate label dependency information into prediction features. Experiments have been conducted on four widely-used benchmark datasets, showing that the proposed model outperforms all strong baselines.
