Multimodal Sentiment Detection Based on Multi-channel Graph Neural Networks (2021.acl-long)
Copied to clipboard
| Challenge: | Existing studies only considered the representation of a single image-text post . Fig. 1 shows that multimodal sentiment expressions have global characteristics . |
| Approach: | They propose a multi-channel Graph Neural Networks with Sentiment-awareness approach for image-text sentiment detection. |
| Outcome: | The proposed approach is effective for image-text sentiment detection on three publicly available datasets. |
Similar Papers
Multi-Channel Convolutional Neural Network for Twitter Emotion and Sentiment Recognition (N19-1)
Copied to clipboard
| Challenge: | Existing methods to analyze tweets are based on lexical features and a multi-channel convolutional neural architecture. |
| Approach: | They propose a neural network which can use different emotion and sentiment indicators such as hashtags, emoticons and emojis present in tweets to improve the performance of emotion and feelings identification. |
| Outcome: | The proposed model can use hashtags, emoticons and emojis present in tweets and improves emotion and sentiment identification. |
Multimodal Multi-loss Fusion Network for Sentiment Analysis (2024.naacl-long)
Copied to clipboard
| Challenge: | This paper examines the optimal selection and fusion of feature encoders across multiple modalities and combines them in one neural network to improve sentiment detection. |
| Approach: | They propose to combine feature encoders across multiple modalities into one neural network to improve sentiment detection. |
| Outcome: | The proposed model achieves state-of-the-art performance for three datasets . it also shows that integrating context significantly improves model performance. |
Multi-Modal Sarcasm Detection via Cross-Modal Graph Convolutional Network (2022.acl-long)
Copied to clipboard
| Challenge: | Existing studies on multimodal sarcasm detection using textual and visual information have been limited to text-only approaches. |
| Approach: | They propose to construct a cross-modal graph for each multi-modal instance to explicitly draw the ironic relations between textual and visual modalities. |
| Outcome: | The proposed model achieves state-of-the-art in multi-modal sarcasm detection. |
Tackling Modality Heterogeneity with Multi-View Calibration Network for Multimodal Sentiment Detection (2023.acl-long)
Copied to clipboard
| Challenge: | Existing studies focus on fusing different features but ignore the challenge of modality heterogeneity. |
| Approach: | They propose a text-guided fusion module with novel Sparse-Attention to reduce the negative impacts of redundant visual elements and a sentiment-based congruity constraint task to calibrate the feature shift in the representation space. |
| Outcome: | The proposed model is competitive against existing methods and achieves state-of-the-art results on two public benchmark datasets. |
D2R: Dual-Branch Dynamic Routing Network for Multimodal Sentiment Detection (2024.emnlp-main)
Copied to clipboard
| Challenge: | Existing methods for multimodal sentiment detection use the same fixed framework to classify the sentiment polarity of image-text pairs. |
| Approach: | They propose a multimodal dynamic interaction model that uses a fixed framework to classify the sentiment polarity of a given imagetext pair. |
| Outcome: | The proposed model outperforms state-of-the-art models on three publicly available datasets. |
Multimodal Fusion with Co-Attention Networks for Fake News Detection (2021.findings-acl)
Copied to clipboard
| Challenge: | Existing methods to detect fake news with textual and visual contents are ineffective because they concatenate unimodal features without considering inter-modality relations. |
| Approach: | They propose to fuse textual and visual features for fake news detection using multimodal co-attention networks to learn inter-dependencies between multimodal features. |
| Outcome: | Extensive experiments on two realworld datasets show that the proposed network outperforms state-of-the-art methods and learns inter-dependencies among multimodal features. |
Affective Knowledge Enhanced Multiple-Graph Fusion Networks for Aspect-based Sentiment Analysis (2022.emnlp-main)
Copied to clipboard
| Challenge: | Existing methods for sentiment analysis ignore the roles of syntax dependency relation labels and affective semantic information in determining the sentiment polarity of social media users. |
| Approach: | They propose a new multi-graph fusion network to leverage the richer syntax dependency relation labels and affective semantic information of words. |
| Outcome: | The proposed model outperforms state-of-the-art methods on three datasets. |
Dynamic and Multi-Channel Graph Convolutional Networks for Aspect-Based Sentiment Analysis (2021.findings-acl)
Copied to clipboard
| Challenge: | . - (EN) |
| Approach: | . - (EN) |
| Outcome: | . - (EN) |
Dynamic Graph Neural ODE Network for Multi-modal Emotion Recognition in Conversation (2025.coling-main)
Copied to clipboard
| Challenge: | Existing graph-based multimodal emotion recognition methods fail to capture dynamic changes in emotions. |
| Approach: | They propose a Dynamic Graph Neural Ordinary Differential Equation Network (DGODE) which combines dynamic changes of emotions to capture temporal dependencies of speakers’ emotions. |
| Outcome: | The proposed model can capture the temporal dependencies caused by dynamic changes in emotions and can improve on two publicly available multimodal emotion recognition datasets. |
MM-GATBT: Enriching Multimodal Representation Using Graph Attention Network (2022.naacl-srw)
Copied to clipboard
| Challenge: | Existing models that use a self-attention mechanism to create graphs with multiple modes ignore interaction between entities, multimodalities, or both. |
| Approach: | They propose a multimodal graph representation learning model that captures relational semantics within one modality and interactions between different modalities. |
| Outcome: | The proposed model outperforms existing models on the MM-IMDb dataset in all aspects of multimodal representation. |