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Aspect-level multimodal sentiment analysis model based on multi-scale feature extraction.

Bocheng Miao1, Changbo Xu2

  • 1Beijing Institute of Graphic Communication, Beijing, 102600, China.

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This study introduces a new multimodal sentiment analysis model that extracts richer features from text and images. The enhanced model improves aspect-level sentiment classification accuracy and effectiveness.

Keywords:
Aspect termsAspect-level multimodal sentiment analysisMulti-scale feature extractionTensor fusion network

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Area of Science:

  • Artificial Intelligence
  • Natural Language Processing
  • Computer Vision

Background:

  • Current multimodal sentiment analysis often overlooks valuable information in intermediate BERT layers.
  • Effective feature extraction from both text and images is crucial for accurate sentiment analysis.

Purpose of the Study:

  • To propose an Aspect-level Multimodal Sentiment Analysis Model with Multi-scale Feature Extraction (AMSAM-MFE).
  • To enhance feature extraction from text and images for improved sentiment analysis.

Main Methods:

  • Developed a Multi-scale Layer module for BERT-based text feature extraction, supervised by aspect terms.
  • Employed a pre-trained Resnest269 model with a Supervision Layer for image feature extraction.
  • Utilized Tensor Fusion Network for comprehensive interaction between visual and textual features.

Main Results:

  • The AMSAM-MFE model demonstrated superior classification effectiveness on Twitter datasets.
  • Achieved improved accuracy and F1 scores in aspect-level multimodal sentiment analysis tasks.
  • Outperformed traditional multimodal sentiment analysis models in experimental comparisons.

Conclusions:

  • Multi-scale feature extraction significantly enhances aspect-level multimodal sentiment analysis.
  • The proposed model offers a more effective approach to sentiment analysis by leveraging intermediate layer information.