Aspect-Based Emotion Analysis and Multimodal Coreference: A Case Study of Customer Comments on Adidas Instagram Posts (2022.lrec-1)
Copied to clipboard
| Challenge: | Aspect-based sentiment analysis of user-generated content has been relatively unexplored in recent years. |
| Approach: | They present a multimodal dataset for Aspect-Based Emotion Analysis (ABEA) they take the first steps in investigating the utility of multimodal coreference resolution in an ABEA framework. |
| Outcome: | The proposed dataset consists of 4,900 comments on 175 images and is annotated with aspect and emotion categories and the emotional dimensions of valence and arousal. |
Similar Papers
A Challenge Dataset and Effective Models for Aspect-Based Sentiment Analysis (D19-1)
Copied to clipboard
| Challenge: | Existing ABSA methods only use one aspect or multiple aspects with the same sentiment polarity . recent studies show that neural network methods can be trained end-to-end and automatically learn important features. |
| Approach: | They propose a large-scale multi-aspect multi-sentiment dataset with two different aspects with different sentiment polarities. |
| Outcome: | The proposed model outperforms the state-of-the-art models on the large-scale dataset . it is based on a novel neural network approach that can be trained end-to-end . |
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. |
Face-Sensitive Image-to-Emotional-Text Cross-modal Translation for Multimodal Aspect-based Sentiment Analysis (2022.emnlp-main)
Copied to clipboard
| Challenge: | Existing models focus on utilizing semantic information in the image but ignore using visual emotional cues. |
| Approach: | They propose a face-sensitive image-to-emotional-text translation method that captures visual emotional cues through facial expressions and selectively matches and fuses with the textual content. |
| Outcome: | The proposed method achieves state-of-the-art results on the Twitter-2015 and Twitter-2017 datasets. |
A Joint Coreference-Aware Approach to Document-Level Target Sentiment Analysis (2024.acl-long)
Copied to clipboard
| Challenge: | Existing work on aspect-based sentiment analysis (ABSA) focuses on sentence level, document level ABSA is more practical and requires holistic document-level understanding capabilities. |
| Approach: | They propose a learning framework to jointly model the DTSA task and the coreference resolution task using ChatGPT. |
| Outcome: | The proposed framework reduces the reliance on annotated coreference information and alleviates evaluation bias caused by missing coreference targets. |
Aspect-based Sentiment Analysis via Synthetic Image Generation (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Recent advances in Aspect-Based Sentiment Analysis (ABSA) have shown promising results, yet the semantics derived solely from textual data remain limited. |
| Approach: | They propose a supervised image generation framework to generate synthetic images with alignment to text and sentiment information. |
| Outcome: | The proposed approach significantly outperforms state-of-the-art methods on multiple benchmark datasets. |
Aspect Extraction Using Coreference Resolution and Unsupervised Filtering (2020.aacl-srw)
Copied to clipboard
| Challenge: | Existing approaches to extract aspects from text are supervised and unsupervised . experimental results show that unsupervised approaches are more accurate than supervised ones . |
| Approach: | They propose to combine a lexical rule-based approach with coreference resolution to improve accuracy. |
| Outcome: | The proposed approach outperforms baseline methods on two benchmark datasets. |
Modelling Context and Syntactical Features for Aspect-based Sentiment Analysis (2020.acl-main)
Copied to clipboard
| Challenge: | Existing approaches to aspect-based sentiment analysis do not fully leverage syntactical information. |
| Approach: | They propose an end-to-end aspect-based sentiment analysis solution that integrates syntactical information with part-of-speech embeddings and dependency-based embeddables to enhance the performance of the aspect extractor. |
| Outcome: | The proposed solution outperforms the state-of-the-art models on SemEval-2014 dataset in both subtasks. |
Sentimental Image Generation for Aspect-based Sentiment Analysis (2025.findings-acl)
Copied to clipboard
| Challenge: | Recent work on textual Aspect-Based Sentiment Analysis (ABSA) has demonstrated promising performance, but limited semantics derived from raw data. |
| Approach: | They propose a method that provides visual semantics to reinforce textual ABSA by adding additional augmentations to the input data. |
| Outcome: | The proposed method can provide visual semantics to reinforce the textual extraction. |
M-ABSA: A Multilingual Dataset for Aspect-Based Sentiment Analysis (2025.emnlp-main)
Copied to clipboard
ChengYan Wu, Bolei Ma, Yihong Liu, Zheyu Zhang, Ningyuan Deng, Yanshu Li, Baolan Chen, Yi Zhang, Yun Xue, Barbara Plank
| Challenge: | Existing studies focus on English-centric aspects of sentiment analysis, limiting scope for multilingual evaluation and research. |
| Approach: | They propose to use a multilingual dataset to analyze aspects with associated sentiment elements in text. |
| Outcome: | The proposed dataset is the most extensive multilingual parallel dataset for ABSA to date. |
OATS: A Challenge Dataset for Opinion Aspect Target Sentiment Joint Detection for Aspect-Based Sentiment Analysis (2024.lrec-main)
Copied to clipboard
| Challenge: | Aspect-based sentiment analysis (ABSA) focuses on understanding sentiments specific to distinct elements within a user-generated review. |
| Approach: | They propose to use Aspect-based sentiment analysis to understand specific aspects of a user-generated review to identify the target entity being reviewed, the aspect to which it belongs, the opinion phrase, and the sentiment expressed toward the aspects. |
| Outcome: | The proposed dataset bridges the gaps observed in existing datasets and sheds light on various ABSA subtasks. |