TMFN: A Target-oriented Multi-grained Fusion Network for End-to-end Aspect-based Multimodal Sentiment Analysis (2024.lrec-main)
Copied to clipboard
| Challenge: | Existing methods for multimodal aspect-based sentiment analysis focus on fusing image regional information and textual words. |
| Approach: | They propose a multimodal aspect-based sentiment analysis method that integrates regional and global image information with global image data. |
| Outcome: | Experiments show that the proposed method outperforms state-of-the-art methods on two benchmark datasets. |
Similar Papers
AoM: Detecting Aspect-oriented Information for Multimodal Aspect-Based Sentiment Analysis (2023.findings-acl)
Copied to clipboard
| Challenge: | Existing methods to extract aspects from text-image pairs and recognize their sentiments are noisy and coarsely establishing image-aspect alignment will interfere with aspect-relevant semantic and sentiment information. |
| Approach: | They propose an Aspect-oriented method to detect aspect-relevant semantic and sentiment information by selecting textual tokens and image blocks that are semantically related to the aspects. |
| Outcome: | The proposed method is superior to existing methods in the field of sentiment analysis. |
DaNet: Dual-Aware Enhanced Alignment Network for Multimodal Aspect-Based Sentiment Analysis (2025.findings-acl)
Copied to clipboard
| Challenge: | Existing methods assume a direct alignment between images and aspects, matching the entire image with a corresponding aspect. Existing algorithms assume 'direct alignment' between images, introducing noise. |
| Approach: | They propose a Dual-Aware Enhanced Alignment Network (DaNet) that can enhance fine-grained multimodal aspect-image alignment and denoising. |
| Outcome: | The proposed system outperforms existing methods in three subtasks and is available on https://github.com/***/DaNet. |
Multi-grained Attention Network for Aspect-Level Sentiment Classification (D18-1)
Copied to clipboard
| Challenge: | Existing approaches to aspect sentiment classification use coarse-grained attention mechanisms . a novel approach captures word-level interaction between aspect and context . |
| Approach: | They propose a novel multi-grained attention network model for aspect level sentiment classification . they use a fine-grounded attention mechanism to capture word-level interaction between aspect and context . |
| Outcome: | The proposed model outperforms the state-of-the-art methods on three datasets . it shows that aspect-level interactions can bring extra useful information and improve performance . |
Autonomous Aspect-Image Instruction a2II: Q-Former Guided Multimodal Sentiment Classification (2024.lrec-main)
Copied to clipboard
| Challenge: | Existing methods to identify sentiment polarities of aspects are limited by the limited multimodal data available. |
| Approach: | They propose to use instruction tuning paradigm to combine language and vision data to combine text and image modalities. |
| Outcome: | The proposed model achieves state-of-the-art on benchmark datasets and in few-shot settings. |
Vision-Language Pre-Training for Multimodal Aspect-Based Sentiment Analysis (2022.acl-long)
Copied to clipboard
| Challenge: | Existing approaches to multimodal Aspect-Based Sentiment Analysis (MABSA) ignore crossmodalalignment and use pre-trained visual and textual models. |
| Approach: | They propose a multimodal multimodal encoder-decoder framework for MABSA that uses a unified multimodal decoder architecture for all the pretrainingand downstream tasks. |
| Outcome: | The proposed framework outperforms state-of-the-art approaches on three MABSA subtasks. |
Learning from Adjective-Noun Pairs: A Knowledge-enhanced Framework for Target-Oriented Multimodal Sentiment Classification (2022.coling-1)
Copied to clipboard
| Challenge: | Existing methods to determine sentiment polarity of opinion target are inconsistent and lack visual attention. |
| Approach: | They propose a framework which can exploit adjective-noun pairs extracted from images to improve visual attention and sentiment prediction capability of the TMSC task. |
| Outcome: | The proposed framework outperforms state-of-the-art on two public datasets. |
Few-shot Joint Multimodal Aspect-Sentiment Analysis Based on Generative Multimodal Prompt (2023.findings-acl)
Copied to clipboard
| Challenge: | Existing studies require massive labeled data to train models for multimodal data analysis. |
| Approach: | They propose a novel multimodal prompt model that captures specific aspect terms in a few-shot scenario. |
| Outcome: | The proposed model outperforms baselines on two MABSA-related tasks on a few-shot dataset. |
Joint Aspect and Polarity Classification for Aspect-based Sentiment Analysis with End-to-End Neural Networks (D18-1)
Copied to clipboard
| Challenge: | a new approach for aspect-based sentiment analysis is proposed . we compare the performance of the proposed approach with pipeline approaches . |
| Approach: | They propose a model for aspect-based sentiment analysis that uses a convolutional neural network and fasttext embeddings to combine the two approaches. |
| Outcome: | The proposed model outperforms pipeline approaches in aspects-based sentiment analysis. |
Joint Aspect Extraction and Sentiment Analysis with Directional Graph Convolutional Networks (2020.coling-main)
Copied to clipboard
| Challenge: | End-to-end aspect-based sentiment analysis uses two sub-tasks to extract aspect terms . experimental results demonstrate the effectiveness of our approach on all datasets . |
| Approach: | They propose to combine aspect extraction and sentiment analysis with encoding syntactic information to improve model's representation of input sentences. |
| Outcome: | The proposed approach achieves state-of-the-art on three benchmark datasets. |
M2DF: Multi-grained Multi-curriculum Denoising Framework for Multimodal Aspect-based Sentiment Analysis (2023.emnlp-main)
Copied to clipboard
| Challenge: | Existing work mainly utilizes image information to improve the performance of MABSA task. |
| Approach: | They propose a multimodal Aspect-based Sentiment Analysis task that uses image information to improve model performance. |
| Outcome: | The proposed framework outperforms state-of-the-art work on three sub-tasks of MABSA. |