# Multi-Modal Sentiment Analysis with Dynamic Attention Fusion

arXiv: https://arxiv.org/abs/2509.22729 · HTML: https://mlsanity.com/paper/2509.22729/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 15 references that are in the map.
- First posted on arXiv 25 Sep 2025.

## Builds on (most cited first, 7 of 7)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [MELD: A Multimodal Multi-Party Dataset for Emotion Recognition in Conversations](https://mlsanity.com/paper/1810.02508/) (2018)
- [FACET: Fairness in Computer Vision Evaluation Benchmark](https://mlsanity.com/paper/2309.00035/) (2023)
- [DynCIM: Dynamic Curriculum for Imbalanced Multimodal Learning](https://mlsanity.com/paper/2503.06456/) (2025)
- [Multimodal Sentiment Analysis: A Survey](https://mlsanity.com/paper/2305.07611/) (2023)
- [Complementary Fusion of Multi-Features and Multi-Modalities in Sentiment Analysis](https://mlsanity.com/paper/1904.08138/) (2019)
- [Deep Learning-based Sentiment Classification: A Comparative Survey](https://mlsanity.com/paper/2312.17253/) (2023)

## Abstract (arXiv)

Traditional sentiment analysis has long been a unimodal task, relying solely on text. This approach overlooks non-verbal cues such as vocal tone and prosody that are essential for capturing true emotional intent. We introduce Dynamic Attention Fusion (DAF), a lightweight framework that combines frozen text embeddings from a pretrained language model with acoustic features from a speech encoder, using an adaptive attention mechanism to weight each modality per utterance. Without any finetuning of the underlying encoders, our proposed DAF model consistently outperforms both static fusion and unimodal baselines on a large multimodal benchmark. We report notable gains in F1-score and reductions in prediction error and perform a variety of ablation studies that support our hypothesis that the dynamic weighting strategy is crucial for modeling emotionally complex inputs. By effectively integrating verbal and non-verbal information, our approach offers a more robust foundation for sentiment prediction and carries broader impact for affective computing applications -- from emotion recognition and mental health assessment to more natural human computer interaction.