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Dynamic Multimodal Sentiment Analysis: Leveraging Cross-Modal Attention for Enabled Classification

Hui Lee, Singh Suniljit, Yong Siang Ong

TL;DR

The paper tackles multimodal sentiment analysis by integrating text, audio, and visual data to capture cross-modal interactions. It evaluates three fusion strategies within a transformer-based ModalityTransformer framework on the CMU-MOSEI dataset, comparing late-stage fusion, early-stage fusion, and cross-modal attention. Early-stage fusion achieves 71.87% accuracy, while multi-headed attention reaches 72.39%, with late fusion lagging at 66.23%, indicating that early fusion substantially improves sentiment classification and attention provides only marginal gains in this setup. The findings highlight the value of early multimodal integration and point to future work in dynamic temporal fusion and adaptive feature weighting for further performance gains.

Abstract

This paper explores the development of a multimodal sentiment analysis model that integrates text, audio, and visual data to enhance sentiment classification. The goal is to improve emotion detection by capturing the complex interactions between these modalities, thereby enabling more accurate and nuanced sentiment interpretation. The study evaluates three feature fusion strategies -- late stage fusion, early stage fusion, and multi-headed attention -- within a transformer-based architecture. Experiments were conducted using the CMU-MOSEI dataset, which includes synchronized text, audio, and visual inputs labeled with sentiment scores. Results show that early stage fusion significantly outperforms late stage fusion, achieving an accuracy of 71.87\%, while the multi-headed attention approach offers marginal improvement, reaching 72.39\%. The findings suggest that integrating modalities early in the process enhances sentiment classification, while attention mechanisms may have limited impact within the current framework. Future work will focus on refining feature fusion techniques, incorporating temporal data, and exploring dynamic feature weighting to further improve model performance.

Dynamic Multimodal Sentiment Analysis: Leveraging Cross-Modal Attention for Enabled Classification

TL;DR

The paper tackles multimodal sentiment analysis by integrating text, audio, and visual data to capture cross-modal interactions. It evaluates three fusion strategies within a transformer-based ModalityTransformer framework on the CMU-MOSEI dataset, comparing late-stage fusion, early-stage fusion, and cross-modal attention. Early-stage fusion achieves 71.87% accuracy, while multi-headed attention reaches 72.39%, with late fusion lagging at 66.23%, indicating that early fusion substantially improves sentiment classification and attention provides only marginal gains in this setup. The findings highlight the value of early multimodal integration and point to future work in dynamic temporal fusion and adaptive feature weighting for further performance gains.

Abstract

This paper explores the development of a multimodal sentiment analysis model that integrates text, audio, and visual data to enhance sentiment classification. The goal is to improve emotion detection by capturing the complex interactions between these modalities, thereby enabling more accurate and nuanced sentiment interpretation. The study evaluates three feature fusion strategies -- late stage fusion, early stage fusion, and multi-headed attention -- within a transformer-based architecture. Experiments were conducted using the CMU-MOSEI dataset, which includes synchronized text, audio, and visual inputs labeled with sentiment scores. Results show that early stage fusion significantly outperforms late stage fusion, achieving an accuracy of 71.87\%, while the multi-headed attention approach offers marginal improvement, reaching 72.39\%. The findings suggest that integrating modalities early in the process enhances sentiment classification, while attention mechanisms may have limited impact within the current framework. Future work will focus on refining feature fusion techniques, incorporating temporal data, and exploring dynamic feature weighting to further improve model performance.
Paper Structure (20 sections, 5 figures, 2 tables)

This paper contains 20 sections, 5 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Approach
  3. Related Work
  4. Data Collection
  5. Preprocessing in CMU MOSEI dataset
  6. Modality Transformer Architecture
  7. Feature Fusion Approach
  8. Approach 0: Late Stage Feature Fusion
  9. Approach 1: Early Stage Feature Fusion
  10. Approach 2: Multi-headed Attention
  11. Experiments and Results
  12. Experimental Setup
  13. Results
  14. Model for Each Modality
  15. Approach 0: Late Stage Feature Fusion Results
  16. ...and 5 more sections

Figures (5)

  • Figure 1: Training and validation loss and accuracy for video model
  • Figure 2: Training and validation loss and accuracy for text model
  • Figure 3: Training and validation loss and accuracy for audio model
  • Figure 4: Training and validation loss and accuracy for early stage feature fusion model
  • Figure 5: Training and validation loss and accuracy for multi-headed attention model