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This paper proposes a multimodal sentiment analysis architecture that integrates text and image data to provide a more comprehensive understanding of sentiments. For text feature extraction, we utilize BERT, a natural language processing model. For image feature extraction, we employ DINOv2, a vision-transformer-based model. The textual and visual latent features are integrated using proposed fusion techniques, namely the Basic Fusion Model, Self-Attention Fusion Model, and Dual-Attention Fusion Model. Experiments on three datasets, the Memotion 7k dataset, MVSA-single dataset, and MVSA-multi dataset, demonstrate the viability and practicality of the proposed multimodal architecture.

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Enhancing Sentiment Analysis Through Multimodal Fusion: A BERT-DINOv2 Approach

  • 会议论文

  • 发表日期：

    2025年

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  • Taoxu Zhao,
  • Meisi Li,
  • Kehao Chen,
  • Liye Wang,
  • Xucheng Zhou,
  • Kunal Chaturvedi,
  • Mukesh Prasad,
  • Ali Anaissi,
  • Ali Braytee

摘要

This paper proposes a multimodal sentiment analysis architecture that integrates text and image data to provide a more comprehensive understanding of sentiments. For text feature extraction, we utilize BERT, a natural language processing model. For image feature extraction, we employ DINOv2, a vision-transformer-based model. The textual and visual latent features are integrated using proposed fusion techniques, namely the Basic Fusion Model, Self-Attention Fusion Model, and Dual-Attention Fusion Model. Experiments on three datasets, the Memotion 7k dataset, MVSA-single dataset, and MVSA-multi dataset, demonstrate the viability and practicality of the proposed multimodal architecture.