mlsanityData to 14 Oct 2026

AoM: Detecting Aspect-oriented Information for Multimodal Aspect-Based Sentiment Analysis

arXiv 2306.01004 ↗First posted 31 May 2023cs.CL cs.AIcited by 9

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Abstract (arXiv)

Multimodal aspect-based sentiment analysis (MABSA) aims to extract aspects from text-image pairs and recognize their sentiments. Existing methods make great efforts to align the whole image to corresponding aspects. However, different regions of the image may relate to different aspects in the same sentence, and coarsely establishing image-aspect alignment will introduce noise to aspect-based sentiment analysis (i.e., visual noise). Besides, the sentiment of a specific aspect can also be interfered by descriptions of other aspects (i.e., textual noise). Considering the aforementioned noises, this paper proposes an Aspect-oriented Method (AoM) to detect aspect-relevant semantic and sentiment information. Specifically, an aspect-aware attention module is designed to simultaneously select textual tokens and image blocks that are semantically related to the aspects. To accurately aggregate sentiment information, we explicitly introduce sentiment embedding into AoM, and use a graph convolutional network to model the vision-text and text-text interaction. Extensive experiments demonstrate the superiority of AoM to existing methods. The source code is publicly released at https://github.com/SilyRab/AoM.

Key facts. Cited by 9 papers in the map (data through 14 Oct 2026). Builds on 12 of its 38 references that are in the map. First posted on arXiv 31 May 2023.

Builds on

12 of 38 references.

References · 12

  1. 2019BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehensioncited by 5,893
  2. 2020MISA: Modality-Invariant and -Specific Representations for Multimodal Sentiment Analysiscited by 265
  3. 2021A Unified Generative Framework for Aspect-Based Sentiment Analysiscited by 97
  4. 2020Position-Aware Tagging for Aspect Sentiment Triplet Extractioncited by 91
  5. 2019Open-Domain Targeted Sentiment Analysis via Span-Based Extraction and Classificationcited by 50
  6. 2021Exploiting BERT For Multimodal Target Sentiment Classification Through Input Space Translationcited by 41
  7. 2021RpBERT: A Text-image Relation Propagation-based BERT Model for Multimodal NERcited by 37
  8. 2014DeepSentiBank: Visual Sentiment Concept Classification with Deep Convolutional Neural Networkscited by 24
  9. 2022BiSyn-GAT+: Bi-Syntax Aware Graph Attention Network for Aspect-based Sentiment Analysiscited by 11
  10. 2022Sentiment Word Aware Multimodal Refinement for Multimodal Sentiment Analysis with ASR Errorscited by 7
  11. 2021Deep Context- and Relation-Aware Learning for Aspect-based Sentiment Analysiscited by 6
  12. 2020A multimodal deep learning approach for named entity recognition from social mediacited by 1

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Cited by year

Citing papers per year: 2024: 4; 2025: 5.

9 papers.

  1. 2024WisdoM: Improving Multimodal Sentiment Analysis by Fusing Contextual World Knowledgecited by 16
  2. 2024Large Language Models Meet Text-Centric Multimodal Sentiment Analysis: A Surveycited by 16
  3. 2024Recent Advances in Multimodal Affective Computing: An NLP Perspectivecited by 13
  4. 2025Exploring Cognitive and Aesthetic Causality for Multimodal Aspect-Based Sentiment Analysiscited by 6
  5. 2025An Empirical Study on Configuring In-Context Learning Demonstrations for Unleashing MLLMs' Sentimental Perception Capabilitycited by 4
  6. 2024RNG: Reducing Multi-level Noise and Multi-grained Semantic Gap for Joint Multimodal Aspect-Sentiment Analysiscited by 3
  7. 2025Enhanced Multimodal Aspect-Based Sentiment Analysis by LLM-Generated Rationalescited by 2
  8. 2025AdaptiSent: Context-Aware Adaptive Attention for Multimodal Aspect-Based Sentiment Analysiscited by 1
  9. 2025CLAMP: Contrastive Learning with Adaptive Multi-loss and Progressive Fusion for Multimodal Aspect-Based Sentiment Analysiscited by 0