Text-image semantic relevance identification for aspect-based multimodal sentiment analysis
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Summary
This summary is machine-generated.This study introduces the Text-Image Semantic Relevance Identification (TISRI) model to improve aspect-based multimodal sentiment analysis (ABMSA) by addressing text-image relevance. TISRI enhances sentiment analysis accuracy for multimodal data.
Area Of Science
- Artificial Intelligence
- Natural Language Processing
- Computer Vision
Background
- Aspect-based multimodal sentiment analysis (ABMSA) identifies sentiment towards specific aspects in multimodal data.
- Existing ABMSA models often fail to account for semantic irrelevance between text and image components.
- Multimodal concatenation in current models can be suboptimal when text and image lack coherence.
Purpose Of The Study
- To propose a novel Text-Image Semantic Relevance Identification (TISRI) model for ABMSA.
- To enhance the accuracy of sentiment analysis in multimodal datasets with potentially irrelevant text-image pairs.
- To improve the robustness of ABMSA models by dynamically managing image information based on semantic relevance.
Main Methods
- Developed a multimodal feature relevance identification module to assess text-image semantic similarity.
- Implemented an image gate mechanism to dynamically control image information input.
- Integrated an attention mechanism for text-aware image representation during multimodal fusion.
- Utilized auxiliary image information to bolster visual feature representation.
Main Results
- The TISRI model demonstrated competitive performance on two ABMSA Twitter datasets.
- Experimental results validated the effectiveness of the proposed semantic relevance identification and dynamic information control methods.
- The attention-based fusion effectively prevented irrelevant image information from interfering with sentiment analysis.
Conclusions
- The TISRI model offers a significant advancement in aspect-based multimodal sentiment analysis.
- Dynamically controlling image information based on semantic relevance is crucial for improving ABMSA performance.
- The proposed approach effectively handles multimodal data where text and image may not be semantically aligned.
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