Sarcasm detection is a complex and challenging task,particularly in the context of Chinese social media,where it exhibits strong contextual dependencies and cultural specificity.To address the limitations of existing ...Sarcasm detection is a complex and challenging task,particularly in the context of Chinese social media,where it exhibits strong contextual dependencies and cultural specificity.To address the limitations of existing methods in capturing the implicit semantics and contextual associations in sarcastic expressions,this paper proposes an event-aware model for Chinese sarcasm detection,leveraging a multi-head attention(MHA)mechanism and contrastive learning(CL)strategies.The proposed model employs a dual-path Bidirectional Encoder Representations from Transformers(BERT)encoder to process comment text and event context separately and integrates an MHA mechanism to facilitate deep interactions between the two,thereby capturing multidimensional semantic associations.Additionally,a CL strategy is introduced to enhance feature representation capabilities,further improving the model’s performance in handling class imbalance and complex contextual scenarios.The model achieves state-of-the-art performance on the Chinese sarcasm dataset,with significant improvements in accuracy(79.55%),F1-score(84.22%),and an area under the curve(AUC,84.35%).展开更多
Using sarcasm on social media platforms to express negative opinions towards a person or object has become increasingly common.However,detecting sarcasm in various forms of communication can be difficult due to confli...Using sarcasm on social media platforms to express negative opinions towards a person or object has become increasingly common.However,detecting sarcasm in various forms of communication can be difficult due to conflicting sentiments.In this paper,we introduce a contrasting sentiment-based model for multimodal sarcasm detection(CS4MSD),which identifies inconsistent emotions by leveraging the CLIP knowledge module to produce sentiment features in both text and image.Then,five external sentiments are introduced to prompt the model learning sentimental preferences among modalities.Furthermore,we highlight the importance of verbal descriptions embedded in illustrations and incorporate additional knowledge-sharing modules to fuse such imagelike features.Experimental results demonstrate that our model achieves state-of-the-art performance on the public multimodal sarcasm dataset.展开更多
Sarcasm detection in Natural Language Processing(NLP)has become increasingly important,partic-ularly with the rise of social media and non-textual emotional expressions,such as images.Existing methods often rely on se...Sarcasm detection in Natural Language Processing(NLP)has become increasingly important,partic-ularly with the rise of social media and non-textual emotional expressions,such as images.Existing methods often rely on separate image and text modalities,which may not fully utilize the information available from both sources.To address this limitation,we propose a novel multimodal large model,i.e.,the PKME-MLM(Prior Knowledge and Multi-label Emotion analysis based Multimodal Large Model for sarcasm detection).The PKME-MLM aims to enhance sarcasm detection by integrating prior knowledge to extract useful textual information from images,which is then combined with text data for deeper analysis.This method improves the integration of image and text data,addressing the limitation of previous models that process these modalities separately.Additionally,we incorporate multi-label sentiment analysis,refining sentiment labels to improve sarcasm recognition accuracy.This design overcomes the limitations of prior models that treated sentiment classification as a single-label problem,thereby improving sarcasm recognition by distinguishing subtle emotional cues from the text.Experimental results demonstrate that our approach achieves significant performance improvements in multimodal sarcasm detection tasks,with an accuracy(Acc.)of 94.35%,and Macro-Average Precision and Recall reaching 93.92%and 94.21%,respectively.These results highlight the potential of multimodal models in improving sarcasm detection and suggest that further integration of modalities could advance future research.This work also paves the way for incorporating multimodal sentiment analysis into sarcasm detection.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
Sarcasm detection in text data is an increasingly vital area of research due to the prevalence of sarcastic content in online communication.This study addresses challenges associated with small datasets and class imba...Sarcasm detection in text data is an increasingly vital area of research due to the prevalence of sarcastic content in online communication.This study addresses challenges associated with small datasets and class imbalances in sarcasm detection by employing comprehensive data pre-processing and Generative Adversial Network(GAN)based augmentation on diverse datasets,including iSarcasm,SemEval-18,and Ghosh.This research offers a novel pipeline for augmenting sarcasm data with Reverse Generative Adversarial Network(RGAN).The proposed RGAN method works by inverting labels between original and synthetic data during the training process.This inversion of labels provides feedback to the generator for generating high-quality data closely resembling the original distribution.Notably,the proposed RGAN model exhibits performance on par with standard GAN,showcasing its robust efficacy in augmenting text data.The exploration of various datasets highlights the nuanced impact of augmentation on model performance,with cautionary insights into maintaining a delicate balance between synthetic and original data.The methodological framework encompasses comprehensive data pre-processing and GAN-based augmentation,with a meticulous comparison against Natural Language Processing Augmentation(NLPAug)as an alternative augmentation technique.Overall,the F1-score of our proposed technique outperforms that of the synonym replacement augmentation technique using NLPAug.The increase in F1-score in experiments using RGAN ranged from 0.066%to 1.054%,and the use of standard GAN resulted in a 2.88%increase in F1-score.The proposed RGAN model outperformed the NLPAug method and demonstrated comparable performance to standard GAN,emphasizing its efficacy in text data augmentation.展开更多
社交媒体上图像和文本数据的快速增长导致人们对多模态讽刺检测问题的关注不断提高。然而,现有基于特征提取融合的检测方法存在一些缺陷:一是大多数方法缺乏多模态检测所需的底层模态对齐能力,二是模态融合过程忽视了模态间的动态关系,...社交媒体上图像和文本数据的快速增长导致人们对多模态讽刺检测问题的关注不断提高。然而,现有基于特征提取融合的检测方法存在一些缺陷:一是大多数方法缺乏多模态检测所需的底层模态对齐能力,二是模态融合过程忽视了模态间的动态关系,三是未能充分利用模态互补性。为此,提出一种基于单模态监督对比学习、多模态融合和多视图聚合预测的检测模型。以CLIP(contrastive language image pre-training)模型作为编码器来增强图像和文本底层编码的对齐效果。结合单模态监督对比学习方法,通过单模态预测来指导模态间的动态关系。然后,设计了全局-局部跨模态融合方法,利用每种模态的语义级表示作为全局多模态上下文与局部单模态特征进行交互,通过多个跨模态融合层提高模态融合效果,并减少了以往局部-局部跨模态融合方法的时间和空间成本。采用多视图聚合预测方法充分利用图像、文本和图文视图的互补性。总之,该模型能有效捕捉多模态讽刺数据的跨模态语义不一致性,在公开数据集MSD上取得了比现有最好方法DMSD-Cl更好的结果。展开更多
Invisible Man is a novel written by Ralph Ellison,talking about how the protagonist struggled in society as an African-American,sought his own identity and sense of belonging,and finally accepted the reality of being...Invisible Man is a novel written by Ralph Ellison,talking about how the protagonist struggled in society as an African-American,sought his own identity and sense of belonging,and finally accepted the reality of being“invisible”to live underground.The metaphors and symbols as key writing techniques in Invisible Man were analyzed in the paper with certain typical examples to reveal their research values for explorations of race issues in the book.The paper particularly highlights the tension embedded in the metaphors and the sarcasm conveyed through the symbols.展开更多
基金granted by Qin Xin Talents Cultivation Program(No.QXTCP C202115),Beijing Information Science&Technology Universitythe Beijing Advanced Innovation Center for Future Blockchain and Privacy Computing Fund(No.GJJ-23),National Social Science Foundation,China(No.21BTQ079).
文摘Sarcasm detection is a complex and challenging task,particularly in the context of Chinese social media,where it exhibits strong contextual dependencies and cultural specificity.To address the limitations of existing methods in capturing the implicit semantics and contextual associations in sarcastic expressions,this paper proposes an event-aware model for Chinese sarcasm detection,leveraging a multi-head attention(MHA)mechanism and contrastive learning(CL)strategies.The proposed model employs a dual-path Bidirectional Encoder Representations from Transformers(BERT)encoder to process comment text and event context separately and integrates an MHA mechanism to facilitate deep interactions between the two,thereby capturing multidimensional semantic associations.Additionally,a CL strategy is introduced to enhance feature representation capabilities,further improving the model’s performance in handling class imbalance and complex contextual scenarios.The model achieves state-of-the-art performance on the Chinese sarcasm dataset,with significant improvements in accuracy(79.55%),F1-score(84.22%),and an area under the curve(AUC,84.35%).
基金National Natural Science Foundation of China,Grant/Award Numbers:61671064,61732005National Key Research and Development Program of China,Grant/Award Number:2018YFC0831700。
文摘Using sarcasm on social media platforms to express negative opinions towards a person or object has become increasingly common.However,detecting sarcasm in various forms of communication can be difficult due to conflicting sentiments.In this paper,we introduce a contrasting sentiment-based model for multimodal sarcasm detection(CS4MSD),which identifies inconsistent emotions by leveraging the CLIP knowledge module to produce sentiment features in both text and image.Then,five external sentiments are introduced to prompt the model learning sentimental preferences among modalities.Furthermore,we highlight the importance of verbal descriptions embedded in illustrations and incorporate additional knowledge-sharing modules to fuse such imagelike features.Experimental results demonstrate that our model achieves state-of-the-art performance on the public multimodal sarcasm dataset.
基金funding partly by the National Natural Science Foundation of China under grant number 61701179.
文摘Sarcasm detection in Natural Language Processing(NLP)has become increasingly important,partic-ularly with the rise of social media and non-textual emotional expressions,such as images.Existing methods often rely on separate image and text modalities,which may not fully utilize the information available from both sources.To address this limitation,we propose a novel multimodal large model,i.e.,the PKME-MLM(Prior Knowledge and Multi-label Emotion analysis based Multimodal Large Model for sarcasm detection).The PKME-MLM aims to enhance sarcasm detection by integrating prior knowledge to extract useful textual information from images,which is then combined with text data for deeper analysis.This method improves the integration of image and text data,addressing the limitation of previous models that process these modalities separately.Additionally,we incorporate multi-label sentiment analysis,refining sentiment labels to improve sarcasm recognition accuracy.This design overcomes the limitations of prior models that treated sentiment classification as a single-label problem,thereby improving sarcasm recognition by distinguishing subtle emotional cues from the text.Experimental results demonstrate that our approach achieves significant performance improvements in multimodal sarcasm detection tasks,with an accuracy(Acc.)of 94.35%,and Macro-Average Precision and Recall reaching 93.92%and 94.21%,respectively.These results highlight the potential of multimodal models in improving sarcasm detection and suggest that further integration of modalities could advance future research.This work also paves the way for incorporating multimodal sentiment analysis into sarcasm detection.
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Sarcasm detection in text data is an increasingly vital area of research due to the prevalence of sarcastic content in online communication.This study addresses challenges associated with small datasets and class imbalances in sarcasm detection by employing comprehensive data pre-processing and Generative Adversial Network(GAN)based augmentation on diverse datasets,including iSarcasm,SemEval-18,and Ghosh.This research offers a novel pipeline for augmenting sarcasm data with Reverse Generative Adversarial Network(RGAN).The proposed RGAN method works by inverting labels between original and synthetic data during the training process.This inversion of labels provides feedback to the generator for generating high-quality data closely resembling the original distribution.Notably,the proposed RGAN model exhibits performance on par with standard GAN,showcasing its robust efficacy in augmenting text data.The exploration of various datasets highlights the nuanced impact of augmentation on model performance,with cautionary insights into maintaining a delicate balance between synthetic and original data.The methodological framework encompasses comprehensive data pre-processing and GAN-based augmentation,with a meticulous comparison against Natural Language Processing Augmentation(NLPAug)as an alternative augmentation technique.Overall,the F1-score of our proposed technique outperforms that of the synonym replacement augmentation technique using NLPAug.The increase in F1-score in experiments using RGAN ranged from 0.066%to 1.054%,and the use of standard GAN resulted in a 2.88%increase in F1-score.The proposed RGAN model outperformed the NLPAug method and demonstrated comparable performance to standard GAN,emphasizing its efficacy in text data augmentation.
文摘社交媒体上图像和文本数据的快速增长导致人们对多模态讽刺检测问题的关注不断提高。然而,现有基于特征提取融合的检测方法存在一些缺陷:一是大多数方法缺乏多模态检测所需的底层模态对齐能力,二是模态融合过程忽视了模态间的动态关系,三是未能充分利用模态互补性。为此,提出一种基于单模态监督对比学习、多模态融合和多视图聚合预测的检测模型。以CLIP(contrastive language image pre-training)模型作为编码器来增强图像和文本底层编码的对齐效果。结合单模态监督对比学习方法,通过单模态预测来指导模态间的动态关系。然后,设计了全局-局部跨模态融合方法,利用每种模态的语义级表示作为全局多模态上下文与局部单模态特征进行交互,通过多个跨模态融合层提高模态融合效果,并减少了以往局部-局部跨模态融合方法的时间和空间成本。采用多视图聚合预测方法充分利用图像、文本和图文视图的互补性。总之,该模型能有效捕捉多模态讽刺数据的跨模态语义不一致性,在公开数据集MSD上取得了比现有最好方法DMSD-Cl更好的结果。
文摘Invisible Man is a novel written by Ralph Ellison,talking about how the protagonist struggled in society as an African-American,sought his own identity and sense of belonging,and finally accepted the reality of being“invisible”to live underground.The metaphors and symbols as key writing techniques in Invisible Man were analyzed in the paper with certain typical examples to reveal their research values for explorations of race issues in the book.The paper particularly highlights the tension embedded in the metaphors and the sarcasm conveyed through the symbols.
