# Explainable Multimodal Aspect-Based Sentiment Analysis with Dependency-guided Large Language Model

arXiv: https://arxiv.org/abs/2601.06848 · HTML: https://mlsanity.com/paper/2601.06848/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 19 of its 45 references that are in the map.
- First posted on arXiv 11 Jan 2026.

## Builds on (most cited first, 19 of 19)

- [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)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution](https://mlsanity.com/paper/2409.12191/) (2024)
- [InstructBLIP: Towards General-purpose Vision-Language Models with Instruction Tuning](https://mlsanity.com/paper/2305.06500/) (2023)
- [Deep contextualized word representations](https://mlsanity.com/paper/1802.05365/) (2018)
- [A Unified Generative Framework for Aspect-Based Sentiment Analysis](https://mlsanity.com/paper/2106.04300/) (2021)
- [Learning Span-Level Interactions for Aspect Sentiment Triplet Extraction](https://mlsanity.com/paper/2107.12214/) (2021)
- [Exploiting BERT For Multimodal Target Sentiment Classification Through Input Space Translation](https://mlsanity.com/paper/2108.01682/) (2021)
- [ZEN 2.0: Continue Training and Adaption for N-gram Enhanced Text Encoders](https://mlsanity.com/paper/2105.01279/) (2021)
- [WisdoM: Improving Multimodal Sentiment Analysis by Fusing Contextual World Knowledge](https://mlsanity.com/paper/2401.06659/) (2024)
- [hyperdoc2vec: Distributed Representations of Hypertext Documents](https://mlsanity.com/paper/1805.03793/) (2018)
- [Few-shot Multimodal Sentiment Analysis based on Multimodal Probabilistic Fusion Prompts](https://mlsanity.com/paper/2211.06607/) (2022)
- [ASTE Transformer Modelling Dependencies in Aspect-Sentiment Triplet Extraction](https://mlsanity.com/paper/2409.15202/) (2024)
- [Enhanced Multimodal Aspect-Based Sentiment Analysis by LLM-Generated Rationales](https://mlsanity.com/paper/2505.14499/) (2025)
- [Revisiting Structured Sentiment Analysis as Latent Dependency Graph Parsing](https://mlsanity.com/paper/2407.04801/) (2024)
- [AdaptiSent: Context-Aware Adaptive Attention for Multimodal Aspect-Based Sentiment Analysis](https://mlsanity.com/paper/2507.12695/) (2025)

## Abstract (arXiv)

Multimodal aspect-based sentiment analysis (MABSA) aims to identify aspect-level sentiments by jointly modeling textual and visual information, which is essential for fine-grained opinion understanding in social media. Existing approaches mainly rely on discriminative classification with complex multimodal fusion, yet they lack explicit sentiment explainability. In this paper, we reformulate MABSA as a generative and explainable task, proposing a unified framework that simultaneously predicts aspect-level sentiment and generates natural language explanations. Based on multimodal large language models (MLLMs), our approach employs a prompt-based generative paradigm, jointly producing sentiment and explanation. To further enhance aspect-oriented reasoning capabilities, we propose a dependency-syntax-guided sentiment cue strategy. This strategy prunes and textualizes the aspect-centered dependency syntax tree, guiding the model to distinguish different sentiment aspects and enhancing its explainability. To enable explainability, we use MLLMs to construct explanation-augmented datasets for fine-tuning. Experiments show that our approach not only achieves overall gains in sentiment classification accuracy, but also produces coherent and aspect-grounded explanations.