Enhancing Long-range Dependency with State Space Model and Kolmogorov-Arnold Networks for Aspect-based Sentiment Analysis (2025.coling-main)
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| Challenge: | Aspect-based Sentiment Analysis (ABSA) evaluates sentiments toward specific aspects of entities within the text. |
| Approach: | They propose a method to enhance long-range dependencies between aspect and opinion words in ABSA by combining attention mechanisms with a syntax-based Graph Convolutional Network and a Mamba-Transformer module. |
| Outcome: | The proposed model outperforms state-of-the-art models on three benchmark datasets. |
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| Challenge: | Existing methods for analyzing aspect terms are focused on extracting semantic information inherent within the sentence. |
| Approach: | They propose a GCNet that explicitly leverages global semantic information to guide context encoding. |
| Outcome: | The proposed model outperforms state-of-the-art methods on three public datasets. |
Improving Aspect-based Sentiment Analysis with Gated Graph Convolutional Networks and Syntax-based Regulation (2020.findings-emnlp)
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Amir Pouran Ben Veyseh, Nasim Nouri, Franck Dernoncourt, Quan Hung Tran, Dejing Dou, Thien Huu Nguyen
| Challenge: | Aspect-based Sentiment Analysis (ABSA) seeks to predict sentiment polarity of input sentences toward a specific aspect. |
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| Outcome: | The proposed model achieves state-of-the-art on three benchmark datasets. |
Aspect-based Sentiment Analysis with Type-aware Graph Convolutional Networks and Layer Ensemble (2021.naacl-main)
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| Challenge: | Existing studies only leverage dependency relations without considering their dependency types . a valid and effective approach is demonstrated on six English benchmark datasets . |
| Approach: | They propose to explicitly utilize dependency types for ABSA with type-aware graph convolutional networks . attention is used in T-GCN to distinguish different edges in the graph and attentive layer ensemble to comprehensively learn from different layers of T-gCN. |
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Enhancing Aspect-level Sentiment Analysis with Word Dependencies (2021.eacl-main)
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| Challenge: | Existing approaches to enhance aspect-level sentiment analysis have omitted syntactic information . experimental results show that our approach outperforms baseline models on all datasets . |
| Approach: | They propose to leverage word dependencies to enhance aspect-level sentiment analysis . they propose to use key-value memory networks to leverage different dependency results . |
| Outcome: | The proposed approach outperforms baseline models on all datasets and achieves state-of-the-art performance on three of them. |
AMR-based Network for Aspect-based Sentiment Analysis (2023.acl-long)
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| Challenge: | Recent studies have used dependency trees to extract relation between aspects and contexts, but there is a potential mismatch between the dependency tree and sentiment classification as a semantic task. |
| Approach: | They propose to replace the syntactic dependency tree with a semantic structure to capture the relation between an aspect and a context. |
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Dual Graph Convolutional Networks for Aspect-based Sentiment Analysis (2021.acl-long)
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| Challenge: | Existing methods to model relationships between aspects and opinion words are inefficient due to informal expressions and complexity of online reviews. |
| Approach: | They propose a dual graph convolutional networks model that considers complementarity of syntax structures and semantic correlations simultaneously. |
| Outcome: | The proposed model outperforms state-of-the-art methods on three public datasets and validates it. |
DAGCN: Distance-based and Aspect-oriented Graph Convolutional Network for Aspect-based Sentiment Analysis (2024.findings-naacl)
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| Challenge: | Recent advances in sentiment analysis tend to interference from local factors such as irrelevant words and edges, hindering the precise identification of opinion words. |
| Approach: | They propose a distance-based syntactic weight and Aspect-Fusion Attention to solve this problem. |
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Aspect Based Sentiment Analysis with Gated Convolutional Networks (P18-1)
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| Challenge: | Aspect-based sentiment analysis can provide more detailed information than general sentiment analysis. |
| Approach: | They propose a model based on convolutional neural networks and gating mechanisms which can selectively output the sentiment features according to the given aspect or entity. |
| Outcome: | The proposed model can selectively output sentiment features according to the given aspect or entity. |
Composition-based Heterogeneous Graph Multi-channel Attention Network for Multi-aspect Multi-sentiment Classification (2022.coling-1)
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| Challenge: | Existing methods for Aspect-based sentiment analysis (ABSA) focus on aspect terms with the same sentiment polarity . current methods focus on sentences with only one aspect term or multiple aspect terms . |
| Approach: | They propose a novel method to model inter-aspect relationships and aspect-context relationships simultaneously using a heterogeneous graph. |
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Aspect-Based Sentiment Analysis with Syntax-Opinion-Sentiment Reasoning Chain (2025.coling-main)
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| Challenge: | Syntactic structures are crucial for capturing aspect-opinion relationships . syntactically based models struggle with linguistic complexities . |
| Approach: | They propose a syntactic-opinion-sentiment reasoning framework that leverages syntaktic information to improve ABSA performance. |
| Outcome: | The proposed framework improves ABSA performance, though smaller LLMs exhibit weaker performance. |