# AdaptiSent: Context-Aware Adaptive Attention for Multimodal Aspect-Based Sentiment Analysis

arXiv: https://arxiv.org/abs/2507.12695 · HTML: https://mlsanity.com/paper/2507.12695/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 46 references that are in the map.
- First posted on arXiv 17 Jul 2025.

## Builds on (most cited first, 15 of 15)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [GLM: General Language Model Pretraining with Autoregressive Blank Infilling](https://mlsanity.com/paper/2103.10360/) (2021)
- [Aspect Level Sentiment Classification with Deep Memory Network](https://mlsanity.com/paper/1605.08900/) (2016)
- [Open-Domain Targeted Sentiment Analysis via Span-Based Extraction and Classification](https://mlsanity.com/paper/1906.03820/) (2019)
- [Vision-Language Pre-Training for Multimodal Aspect-Based Sentiment Analysis](https://mlsanity.com/paper/2204.07955/) (2022)
- [Few-shot Joint Multimodal Aspect-Sentiment Analysis Based on Generative Multimodal Prompt](https://mlsanity.com/paper/2305.10169/) (2023)
- [AoM: Detecting Aspect-oriented Information for Multimodal Aspect-Based Sentiment Analysis](https://mlsanity.com/paper/2306.01004/) (2023)
- [M2DF: Multi-grained Multi-curriculum Denoising Framework for Multimodal Aspect-based Sentiment Analysis](https://mlsanity.com/paper/2310.14605/) (2023)
- [A Novel Energy based Model Mechanism for Multi-modal Aspect-Based Sentiment Analysis](https://mlsanity.com/paper/2312.08084/) (2023)
- [Syntax-aware Hybrid prompt model for Few-shot multi-modal sentiment analysis](https://mlsanity.com/paper/2306.01312/) (2023)

## Built on by (most cited first, 1 of 1)

- [Explainable Multimodal Aspect-Based Sentiment Analysis with Dependency-guided Large Language Model](https://mlsanity.com/paper/2601.06848/) (2026)

## Abstract (arXiv)

We introduce AdaptiSent, a new framework for Multimodal Aspect-Based Sentiment Analysis (MABSA) that uses adaptive cross-modal attention mechanisms to improve sentiment classification and aspect term extraction from both text and images. Our model integrates dynamic modality weighting and context-adaptive attention, enhancing the extraction of sentiment and aspect-related information by focusing on how textual cues and visual context interact. We tested our approach against several baselines, including traditional text-based models and other multimodal methods. Results from standard Twitter datasets show that AdaptiSent surpasses existing models in precision, recall, and F1 score, and is particularly effective in identifying nuanced inter-modal relationships that are crucial for accurate sentiment and aspect term extraction. This effectiveness comes from the model's ability to adjust its focus dynamically based on the context's relevance, improving the depth and accuracy of sentiment analysis across various multimodal data sets. AdaptiSent sets a new standard for MABSA, significantly outperforming current methods, especially in understanding complex multimodal information.