基于依存关系引导大语言模型的可解释多模态方面级情感分析

Computer Science > Computation and Language arXiv:2601.06848 (cs) [Submitted on 11 Jan 2026 (v1), last revised 18 Sep 2026 (this version, v2)] Title:Explainable Multimodal Aspect-Based Sentiment Analysis with Dependency-guided Large Language Model Authors:Zhongzheng Wang, Yuanhe Tian, Hongzhi Wang, Yan Song View a PDF of the paper titled Explainable Multimodal Aspect-Based Sentiment Analysis with Dependency-guided Large Language Model, by Zhongzheng Wang and Yuanhe Tian and Hongzhi Wang and Yan Song View PDF HTML (experimental) Abstract: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. Comments: Subjects: Computation and Language (cs.CL) Cite as: arXiv:2601.06848 [cs.CL] (or arXiv:2601.06848v2 [cs.CL] for this version) https://doi.org/10.48550/arXiv.2601.06848 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Zhongzheng Wang [view email] [v1] Sun, 11 Jan 2026 10:41:33 UTC (726 KB) [v2] Fri, 18 Sep 2026 12:11:06 UTC (3,765 KB) Full-text links: Access Paper: View a PDF of the paper titled Explainable Multimodal Aspect-Based Sentiment Analysis with Dependency-guided Large Language Model, by Zhongzheng Wang and Yuanhe Tian and Hongzhi Wang and Yan Song View PDF HTML (experimental) TeX Source view license Current browse context: cs.CL new | recent | 2026-01 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv (What is alphaXiv?) Links to Code Toggle CatalyzeX Code Finder for Papers (What is CatalyzeX?) DagsHub Toggle DagsHub (What is DagsHub?) GotitPub Toggle Gotit.pub (What is GotitPub?) Huggingface Toggle Hugging Face (What is Huggingface?) ScienceCast Toggle ScienceCast (What is ScienceCast?) Demos Demos Replicate Toggle Replicate (What is Replicate?) Spaces Toggle Hugging Face Spaces (What is Spaces?) Spaces Toggle TXYZ.AI (What is TXYZ.AI?) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower (What are Influence Flowers?) Core recommender toggle CORE Recommender (What is CORE?) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs. Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)