Advances in Multimodal Sentiment and Sarcasm Detection

The recent advancements in multimodal sentiment analysis and sarcasm detection have shown significant progress, particularly in addressing the complexities of human language and the integration of multiple data modalities. Researchers are increasingly focusing on developing models that can effectively fuse textual, visual, and structural data to enhance the accuracy and generalizability of sentiment and sarcasm detection. Novel approaches are being introduced to handle data uncertainty, spurious correlations, and the integration of contextual and network-aware features, which are crucial for improving the robustness of these models. Notably, the use of contrastive learning and attention mechanisms in multimodal frameworks is proving to be effective in capturing the intricate relationships between different data types. These innovations are not only advancing the state-of-the-art but also paving the way for more sophisticated applications in natural language processing and social media analysis.

Noteworthy Papers:

  • A novel method integrating multimodal incongruities via contrastive learning significantly enhances the generalizability of sarcasm detection models.
  • A data uncertainty-aware approach for multimodal aspect-based sentiment analysis achieves state-of-the-art performance by prioritizing high-quality samples.
  • An ensemble architecture that incorporates graph information and social interactions improves fake news detection, outperforming existing models.

Sources

Was that Sarcasm?: A Literature Survey on Sarcasm Detection

Multi-View Incongruity Learning for Multimodal Sarcasm Detection

Data Uncertainty-Aware Learning for Multimodal Aspect-based Sentiment Analysis

GETAE: Graph information Enhanced deep neural NeTwork ensemble ArchitecturE for fake news detection

Multimodal Sentiment Analysis Based on BERT and ResNet

Acquired TASTE: Multimodal Stance Detection with Textual and Structural Embeddings

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