Summary of Evaluation Of Data Inconsistency For Multi-modal Sentiment Analysis, by Yufei Wang et al.
Evaluation of data inconsistency for multi-modal sentiment analysis
by Yufei Wang, Mengyue Wu
First submitted to arxiv on: 5 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper tackles the issue of emotion semantic inconsistency in multi-modal sentiment analysis (MSA). MSA is a task that involves analyzing sentiment expressed across various modalities, such as text, audio, and videos. The problem lies in the subtle and nuanced expressions of human beings, leading to inconsistencies that can hinder artificial agents’ predictions. To address this challenge, the authors introduce a modality conflicting test set and evaluate the performance of traditional MSA models and multi-modal large language models (MLLMs). The results show significant performance degradation for traditional models when faced with semantically conflicting data, highlighting the limitations of these models. Moreover, the study reveals that MLLMs struggle to handle multi-modal emotion analysis. This research presents a new challenge and provides valuable insights for future development in sentiment analysis systems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine trying to understand how someone is feeling based on what they’re saying and doing. It’s not just about understanding the words, but also the tone and facial expressions. This is called multi-modal sentiment analysis (MSA). The problem is that people can express emotions in different ways, making it hard for machines to figure out how we’re really feeling. In this study, researchers created a special test set to see how well computers do when faced with these inconsistencies. They found that some computer models are good at understanding one type of expression, but struggle when faced with others. This research helps us understand what makes sentiment analysis challenging and will hopefully lead to better machines that can truly understand our emotions. |
Keywords
» Artificial intelligence » Multi modal
