# Syntax-aware Hybrid prompt model for Few-shot multi-modal sentiment analysis

arXiv: https://arxiv.org/abs/2306.01312 · HTML: https://mlsanity.com/paper/2306.01312/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 36 references that are in the map.
- First posted on arXiv 2 Jun 2023.

## Builds on (most cited first, 13 of 13)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Making Pre-trained Language Models Better Few-shot Learners](https://mlsanity.com/paper/2012.15723/) (2020)
- [It's Not Just Size That Matters: Small Language Models Are Also Few-Shot Learners](https://mlsanity.com/paper/2009.07118/) (2020)
- [KnowPrompt: Knowledge-aware Prompt-tuning with Synergistic Optimization for Relation Extraction](https://mlsanity.com/paper/2104.07650/) (2021)
- [Aspect Based Sentiment Analysis with Gated Convolutional Networks](https://mlsanity.com/paper/1805.07043/) (2018)
- [Double Embeddings and CNN-based Sequence Labeling for Aspect Extraction](https://mlsanity.com/paper/1805.04601/) (2018)
- [Syntax-Aware Aspect Level Sentiment Classification with Graph Attention Networks](https://mlsanity.com/paper/1909.02606/) (2019)
- [Conditional Augmentation for Aspect Term Extraction via Masked Sequence-to-Sequence Generation](https://mlsanity.com/paper/2004.14769/) (2020)
- [Syntax-Aware Aspect-Level Sentiment Classification with Proximity-Weighted Convolution Network](https://mlsanity.com/paper/1909.10171/) (2019)
- [Embarrassingly Simple Unsupervised Aspect Extraction](https://mlsanity.com/paper/2004.13580/) (2020)
- [Sentiment Word Aware Multimodal Refinement for Multimodal Sentiment Analysis with ASR Errors](https://mlsanity.com/paper/2203.00257/) (2022)
- [A Generative Language Model for Few-shot Aspect-Based Sentiment Analysis](https://mlsanity.com/paper/2204.05356/) (2022)
- [Adaptive Prompt Learning-based Few-Shot Sentiment Analysis](https://mlsanity.com/paper/2205.07220/) (2022)

## Built on by (most cited first, 2 of 2)

- [AdaptiSent: Context-Aware Adaptive Attention for Multimodal Aspect-Based Sentiment Analysis](https://mlsanity.com/paper/2507.12695/) (2025)
- [Co-AttenDWG: Co-Attentive Dimension-Wise Gating and Expert Fusion for Multi-Modal Offensive Content Detection](https://mlsanity.com/paper/2505.19010/) (2025)

## Abstract (arXiv)

Multimodal Sentiment Analysis (MSA) has been a popular topic in natural language processing nowadays, at both sentence and aspect level. However, the existing approaches almost require large-size labeled datasets, which bring about large consumption of time and resources. Therefore, it is practical to explore the method for few-shot sentiment analysis in cross-modalities. Previous works generally execute on textual modality, using the prompt-based methods, mainly two types: hand-crafted prompts and learnable prompts. The existing approach in few-shot multi-modality sentiment analysis task has utilized both methods, separately. We further design a hybrid pattern that can combine one or more fixed hand-crafted prompts and learnable prompts and utilize the attention mechanisms to optimize the prompt encoder. The experiments on both sentence-level and aspect-level datasets prove that we get a significant outperformance.