Papers with multimodal sentiment detection
NLP for Conversations: Sentiment, Summarization, and Group Dynamics (C18-3)
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| Challenge: | a tutorial focuses on computational models for conversational structure, summarization and sentiment detection, and group dynamics. |
| Approach: | a tutorial will provide examples of specific NLP tasks for conversational structure, summarization and sentiment detection, and group dynamics. |
| Outcome: | The tutorial focuses on the three areas of conversational structure, summarization and sentiment detection, and group dynamics. |
Multimodal Sentiment Detection Based on Multi-channel Graph Neural Networks (2021.acl-long)
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| 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. |
CLMLF:A Contrastive Learning and Multi-Layer Fusion Method for Multimodal Sentiment Detection (2022.findings-naacl)
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| Challenge: | Existing methods for multimodal sentiment detection do not consider token-level feature fusion. |
| Approach: | They propose a method for multimodal sentiment detection using a combination of text and image to encode and fuse token-level features. |
| Outcome: | The proposed method can fuse multimodal features with token-level features on three publicly available multimodal datasets. |
D2R: Dual-Branch Dynamic Routing Network for Multimodal Sentiment Detection (2024.emnlp-main)
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| 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. |